Water Management in Hydraulic Fracturing-A Planning
and Decision Optimization Platform
By
MASSACHUSETTS WI
OF TECHNOLOGY
Neha Mehta
OCT 0 7 2014
B.Tech. in Pulp and Paper Technology
Indian Institute of Technology, Roorkee, India, 2010
LIBRARIES
M.S. in Chemical Engineering
University of California, Berkeley, 2011
SUBMITTED TO THE ENGINEERING SYSTEMS DIVISION IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN TECHNOLOGY AND POLICY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2014
C 2014 Massachusetts Institute of Technology. All rights reserved.
Signature of Author:
Signature redacted
Engineering Systems Division
August 21, 2014
Certified by:Francis'
Accepted by:
S
Francis 0' Sullivan
Director of Research, MIT Energy Initiative
Thesis Supervisor
Signature red acted
Dava J. Newman
Profes~or of Aeronautics and Astronautics and Engineering Systems
Director, Technology and Policy Program
ETLtE
Water Management in Hydraulic Fracturing- A Planning and Decision
Optimization Platform
By
Neha Mehta
Submitted to the Engineering System Division
on August 21st, 2014, in partial fulfillment
of the requirements for the degree of
Masters of Science in Technology and Policy
Abstract
Recent developments in hydraulic fracturing technology have enabled cost-effective production of
unconventional resources, particularly shale gas in the U.S. The process of hydraulic fracturing is
water intensive, requiring 4-7 million gallons of water per well, to which a range of chemicals
must also be added in order to produce an effective fracturing fluid. Following a fracturing
stimulation, anywhere from 10-40% of the injected volume of the water flows back to the surface
as a polluted stream of wastewater. This polluted stream of water and the overall inefficient use of
water in the hydraulic fracturing process has resulted in a number of negative environmental
consequences, specifically surrounding ground and surface water quality and quantity. In
considering how to minimize the environmental impacts of hydraulic fracturing, effectively
managing water throughout the entire hydraulic fracturing water cycle (water acquisition and
disposal) is obviously critical. This dissertation articulates a GIS based optimization model that
has been developed to optimize water management planning for unconventional oil and gas
production. The model enables a diverse set of stakeholders to develop customized water
management strategies based on the geological characteristics and water infrastructure of any
given play. The model comprises of a front end GIS interface and a back end optimization engine,
designed to minimize the overall system cost of water handling as well as minimizing the overall
water footprint of the system. Altogether, it is a powerful decision making tool, which allows the
operators to optimize and analyze the temporal and spatial variations in flowback, and produced
water management and provide an operationally convenient method to access and share the model
analysis. From a regulatory perspective, the modeling framework provides a comprehensive
template for a water management plan and could be used as a basis to develop tailored, customized
regional solutions that can incorporate the inherent heterogeneity widespread across today's oil
and gas plays.
Thesis Supervisor: Francis 0' Sullivan
Title: Director of Research, MIT Energy Initiative
Acknowledgements
I want to take this opportunity to thank my advisor Francis 0' Sullivan, MIT Energy Initiative.
You have shown tremendous confidence in my capabilities, because of which I am able to expand
my spectrum of skills to an entirely different level. Thanks for your continuous support and
guidance, and I hope to seek it further during the course of my doctoral program here at MIT.
I would also like to extend my token of gratitude towards the ESD staff, especially Barbara S
DeLaBarre for being such a wonderful listener to all my administrative hassles. Thank you for
constantly keeping check on us whether it was for submission of the thesis proposal, or fall
registration.
I would also like to extend several MIT faculty members especially Prof. John Lienhard, Dr.
Roland Pellenq, Prof. James B. Orlin, Prof. Dennis McLaughlin and Andrew Cockerill, who took
time to provide feedback on my research and gave me an opportunity to share it with their groups.
The results of this thesis would not have been reflective of a practical setting without the generous
sharing of data by the Texas Water Development Board. I appreciate your time and efforts in
finding the data.
The list would not end without mentioning my friends who made sure that I had some fun time
through this journey. Prateek Verma, Ankita Vyas, Chandani Limbad thanks a ton for being there
for me. And, lastly, my family is what gives me the strength and inspiration to keep going.
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Table of Contents
1. Introduction...............................................................................................................................
1
1.1. M otivation............................................................................................................................
1.2. Scope....................................................................................................................................
2
4
1.3. Thesis structure....................................................................................................................
5
Section 1-Background...................................................................................................................
7
2. Hydraulic fracturing w ater cycle .........................................................................................
8
2.1. W ater Acquisition........................................................................................................
8
2.2. Chem ical Mixing ...............................................................................................................
10
2.3. W ater Injection ..................................................................................................................
2.4. HF Wastew ater Recovery .............................................................................................
2.5. HF Wastew ater M anagem ent..........................................................................................
12
14
17
3. Regulatory framework around hydraulic fracturing......................................................
19
3.1. Federal Regulations ...........................................................................................................
3.1.1. Clean W ater Act (CW A) .....................................................................................................
3.1.2. Safe Drinking W ater Act (SDW A) .......................................................................................
3.2. State Regulations ...............................................................................................................
3.2.1. W ater Procurement and Use.................................................................................................
3.2.2. Fracture Fluid Chemical Disclosure.....................................................................................
3.2.3. Wastewater disposal requirements .......................................................................................
3.3. Regulatory conundrum ...................................................................................................
19
19
20
21
23
24
24
:..25
4. C hallenges in m anaging HF wastew ater............................................................................
27
4.1. Tem poral and spatial constraints ....................................................................................
4.2. Operational constraints ..................................................................................................
4.3. Regulatory constraints ....................................................................................................
4.4. Evaluation of wastewater management frameworks .....................................................
4.4.1. Integrated multi-criteria decision .........................................................................................
4.4.2. Produced Water M anagement Information System .............................................................
4.4.3. W ater Decision Tree.............................................................................................................
27
28
29
30
30
30
30
Section 2- M odel Developm ent .............................................................................................
32
5. Modeling approach for management of HF wastewater.................................................
33
5.1. M odel Fram ew ork..............................................................................................................
5.2. Techniques and M ethodology .........................................................................................
5.2.1. Determination of HF wastewater quality .............................................................................
5.2.2. Determination of HF wastewater quantity ..........................................................................
5.2.3. Field development data ........................................................................................................
5.2.4. M odeling transport network .................................................................................................
33
35
35
36
37
37
6. Prelim inary Engineering Design A nalysis.......................................................................
39
6.1. Tw o-Stage Lim e Softening Plant..................................................................................
6.1.1. Cost data sources...............................................................................................................
6.1.2. Process design consideration..............................................................................................
39
40
43
6.2. Desalination Plant..............................................................................................................
6.2.1. Reverse Osmosis (RO)........................................................................................................
44
45
Section 3- Analysis and Recom m endation............................................................................
48
7. Case Study Description ......................................................................................................
49
7.1. Barnett Shale......................................................................................................................
7.1.1. Water Supply............................................................................................................................50
7.1.2. HF W astewater quality and quantity ..................................................................................
7.1.3. Influent water quality ..........................................................................................................
7.1.4. HF W astewater Management ..............................................................................................
7.1.5. Economic inputs.......................................................................................................................60
7.2. Results................................................................................................................................
49
7.3. Sensitivity analysis ............................................................................................................
64
7.3.1. Influence of influent water quality .......................................................................................
7.3.2. Influence of water availability in the region.........................................................................
8. Policy recom m endations.....................................................................................................
8.1. N on-uniform policy fram ework.........................................................................................
8.2. M arket based policy approaches.....................................................................................
8.3. Water m anagem ent planning .........................................................................................
53
57
58
61
64
65
68
68
69
70
9. Future w ork.............................................................................................................................
71
10. Sum m ary................................................................................................................................
71
References....................................................................................................................................
73
List of Appendix..........................................................................................................................
79
Appendix A : M ajor Shale Plays in the United States............................................................
Appendix B: Description of fracture fluid additives ...........................................................
Appendix C: HF W astewater Characterization ....................................................................
Appendix D : Detailed techno-econom ic analysis of RO plant............................................
Appendix E: Decay constant for estimating the volumetric rate of production of HF
wastewater ................................................................................................................................
Appendix F: Cost summary of different Two Stage Lime Softening Plant...........................
Appendix H : Linear Optim ization Program ing, M atlab TM .......................... . . . . . .. . . . . . .. . . . . . .. . . . .
80
81
85
87
89
90
91
List of Figures
Figure 1: Hydraulic fracturing process overview ......................................................................
1
Figure 2: Hydraulic fracturing water cycle (Source: EPA) .......................................................
5
Figure 3: The maps displays the U.S. drought monitor for Texas. The completed wells are shown
in black dots, overlaid by bands of reds and yellows, with red bands depicting areas of highest
w ater stress......................................................................................................................................
9
Figure 4: Degradation of a gel by a breaking agent. The breaker used in this case is ammonium
persulfate and the gel is made from Guar Gum. The degradation mechanism is a free radical
degradation reaction and prone to exhibit reduced free radical activity due to inhibition of the free
radicals by the degraded fragm ents..........................................................................................
13
Figure 5: Fracturing Fluid Disclosure requirement across states..............................................
24
Figure 6: Temporal trends in wastewater volume produced......................................................
27
Figure 7: Conceptual layout of the model framework.............................................................
33
Figure 8: Workflow for predicting the wastewater quality in fracturing operations ................. 35
Figure 9: Schematic of a two stage lime soda-ash softening plant ............................................
39
Figure 10: Relationship between cost of wastewater treatment and number of wells completed
annu ally .........................................................................................................................................
43
Figure 11: Schematic of a basic RO loop (source: Puretec Industrial Water)........................... 45
Figure 12: Estimate of the groundwater/surface water split in different shale regions. The base map
shows the outline of major aquifers and major rivers in Texas. SW stands for Surface water source
and GW stands for groundw ater sources......................................................................................
51
Figure 13: Groundwater location and major rivers in Johnson.................................................
51
Figure 14: Surface river withdrawal points .............................................................................
52
Figure 15: Road network connecting gas wells to groundwater wells ......................................
52
Figure 16: Salinity profiles in Texas based on spatial interpolation..........................................
54
Figure 17: Calcium profiles in Texas based on spatial interpolation.......................................
54
Figure 18: Time series of salinity profiles of HF wastewater...................................................
55
Figure 19: Hardness time-series profile for HF wastewater .....................................................
55
Figure 20: Turbidity time-series profile for HF wastewater.....................................................
56
Figure 21: Rate of wastewater production in different wells...................................................
57
Figure 22: The figure on left shows the transportation network from gas wells to deep-water
injection wells whereas the figure on the right shows the transportation networks from gas wells
to desalination plants (RO ). .....................................................................................................
60
Figure 23: Water management plan for the field development ................................................
62
Figure 24: The aggregate breakdown of the modeled plan for the three influent water qualities. 64
Figure 25: Impact of influent water quality variation in fracturing operation on the water
m anagem ent costs.........................................................................................................................
65
Figure 26: Aggregate water management plan .........................................................................
66
Figure 27: Impact on cost of water management.......................................................................
66
Figure 28: Major shale plays in the U.S. .................................................................................
80
Figure
Figure
Figure
Figure
Figure
81
81
82
83
83
29:
30:
31:
32:
33:
Structural form of gur gum.....................................................................................
Structural form of ammonium persulfate ...............................................................
Structural form of Glutaraldehyde ...........................................................................
Structural form of borate salts ................................................................................
Structure form of Polyacrylamide ...........................................................................
List of tables
Table 1: Commonly used additives in a fracturing stimulation.................................................
10
Table 2: Analytical water characterization of influent water and after addition of fracture additives
.......................................................................................................................................................
11
Table 3: The reported cumulative wastewater volume in different counties in Pennsylvania..... 14
Table 4: An example of HF wastewater quality ......................................................................
16
Table 5: Summary of potential additive compatibility concerns caused due to presence of high
concentration of pollutant in influent water..............................................................................
28
Table 6: General cost equations for UFSCC..............................................................................
41
Table 7: General cost equations for Gravity filter ....................................................................
41
Table 8: General cost equations for chemical feeders ...............................................................
42
Table 9: RO model design parameters in DEEPTM..................................... ........................... . . 46
Table 10: Shale has field completion schedule.........................................................................
49
Table 11: Ultimate wastewater quality parameters for selected well sites ............................... 53
Table 12: The influent water quality for formulating fracture fluid used in the model............ 58
Table 13: The distance of nearest injection wells and desalination plants from the gas wells..... 59
Table 14: Summary of influent water quality in Barnett shale play .........................................
85
Table 15: Summary of water quality parameters of a blended fluid in Barnett shale .............. 86
1. Introduction
The U.S. natural gas industry, and by extension the industry globally has borne witness to
tremendous change over the past decade. During this period, U.S. natural gas production levels
have risen from a twenty year low of 18 TCF1 (510 BCM 2) in 2005, to an all-time high of 24 TCF
(680 BCM) in 20123. At the same time, natural gas prices have fallen to levels not seen since the
period immediately following the U.S. gas market deregulation in the mid-nineties. The underlying
driver of these dynamics has been the very rapid growth in the production of unconventional
natural
gas resources, and
in particular,
shale gas resources
-
historically considered
unrecoverable.
Figure
1: Hydraulic fracturing process overview
Some of the most active shale plays in the United States are the Barnett Shale, the
Haynesville/Bossier Shale, the Antrim Shale, the Fayetteville Shale, the Marcellus Shale, and the
New Albany Shale (see Appendix A: Major Shale Plays in the United States)~. The shale resource
is a collection of many hydrocarbon-prone mud rock formations with a diverse set of geological,
geomechanical, geochemical and petrophysical characteristics. Theses rock formations contain
' TCF: trillion cubic feet
billion cubic meter
2 BCM:
'3
Marketed production as reported by U.S. Energy Information Administration, June 2013
C
organic matter (kerogen) which is the source material for all hydrocarbon resources 2. Over time,
the rock matures, and hydrocarbons are produced from the kerogen 2,3. These hydrocarbons may
later migrate from the source rock through existing rock fractures, in either liquid or gaseous state,
and reach the earth's surface. However, in the case of shale gas resources, the very low permeability
of the source rock inhibits the movements of hydrocarbon and prevents them from entering the
zone of migration towards the surface. Absorption of hydrocarbon onto organic matter in the
subsurface environment further limits the mobility of hydrocarbon in subsurface environment.
Therefore, owing to very low rock matrix permeability and unfavorable gas storage and
distribution properties in the shale rock strata, historically it was not feasible to produce
hydrocarbons at economically feasible rates from shale rocks.
Despite unfavorable geological characteristics of shale rocks, technical advances in the areas of
drilling and reservoir stimulation over the years have enabled the recovery of hydrocarbons from
shale rocks, particularly shale gas. The majority of supply of shale gas comes from wells drilled
with horizontal bores and subjected to large-scale hydraulic fracture stimulation. Horizontal bores
allow the wellbore to come in contact with a significantly larger surface area in comparison to a
vertical well. Hydraulic fracturing is a well stimulation technique through which a large number
of fractures are created mechanically in the rock strata, thus allowing the hydrocarbons to be
released from the formations. Hydraulic fracturing of these formations results in a significant
enhancement in well productivity, and today it is a ubiquitous reservoir stimulation technique across
shale and other low and not so low permeability formations. Regardless of what type of formation is
being stimulated, the fundamental processes involved in hydraulic fracturing remain the same.
Large volumes of fluid is pumped (water is the commonly used) into the well bore at a sufficient
rate to generate a pressure differential between the well bore and the reservoir. This causes stresses
around the well bore to increase beyond the tensile stress of the rock, at which point it splits or
"fractures" (see Figure 1). The newly formed fractures are supported by the proppant materials
which ensure enhanced permeability as the well is brought to production mode.
1.1. Motivation
In the current evolving energy sector, the economic exploitation of shale resources has
unsurprisingly projected a more gas-centric future than was envisioned even a few years ago.
However, the growth in gas, which some have styled a "revolution," is not without its issues. The
2
widespread use of hydraulic fracturing technology to extract hydrocarbons from shale formations
has resulted in a number of potentially harmful environmental and public health consequences
especially related to water. Contemporary hydraulic fracturing treatments are water intensive,
requiring several million gallons of water per well. However, when compared to other energy
production processes, the water intensity of shale gas production (including drilling, fracturing,
extraction, and processing) appears relatively less, which is between 0.6 and 3.8 gallons of water
per Million British Thermal Units (MMBtu) produced4 . The reason for the low water intensity of
shale gas production process lies in the fundamental differences in the water requirements of
hydraulic fracturing operations when compared to other energy production processes. Firstly,
Hydraulic fracturing water consumption is primarily front-loaded, i.e. during the completion
phase. In this stage, large volumes of water are procured over a relatively short span of time,
creating a transient stress on the local water resources. Secondly, the companies engaged in
hydraulic fracturing operations have to rely on local water supply of water as these wells are being
developed in a limited geographic extent. Together these factors can adversely affect the regional
water resources. Furthermore, in case of rapid and concentrated well development, the cumulative
water needs of multiple drilling and fracturing operations may be significant, particularly in areas
with water constraints and competing water demands for domestic, agricultural, and thermoelectric use.
Water quantity concerns related to shale gas development are intensified furthermore by hydraulic
fracturing wastewater management concerns. In the days following hydraulic fracturing of a well,
large volumes of effluent flow from a well; a range of terms are used to describe this effluent
including, but not limited to; "hydraulic fracturing wastewater," "flowback water," and "produced
water." For the purpose of this dissertation, the effluent will be referred as "hydraulic fracturing
wastewater (HF wastewater)."
The management of HF wastewater is one of the key operational and environmental challenges
associated with contemporary onshore oil and gas operations. The reasons for this are twofold: 1.
the volumes of effluent can be large - often 10-40% of the injected volume flows from a well as
effluent during the two weeks immediately post a hydraulic fracturing operation and 2. The
effluent that flows from the hydraulically fractured well is very polluted, and the nature and
composition of this pollution vary with time 5-8. Inadequate management of HF wastewater could
3
result in contamination of freshwater supplies, community disruption and air pollution from truck
traffic related to gas development (e.g. Truck transport of HF wastewater and fracturing fluids on
and off the well site), and eco-system disruption4 . Therefore, as hydraulic fracturing becomes
ubiquitous across onshore oil and gas development in North America, even greater focus must be
placed on the safe management of HF wastewater and the overall need to absolutely minimize the
environmental footprint of the process.
1.2. Scope
The study is limited to onshore oil and gas development in North America, and more specifically
the United States. The focus of the study is to explore a solution space for mitigating environmental
externalities related to shale oil and gas development water cycle (see Figure 2). Water
externalities not covered in this study are during the chemical mixing stage and well injection
stages. Some example of issues that can occur during these stages are on-site spill of fracturing
fluids into surface and groundwater resources, mobilization of subsurface materials into aquifers,
and formation fluid displacement into aquifers. These externalities are equally significant;
however limited availability of documented reports compromises the understanding the associated
issues. The adverse impacts of hydraulic fracturing operations are not limited to water, but also
spans across the air (e.g. Fugitive emissions), land (e.g. Road traffic and acreage of a well pad)
and community impacts (e.g. Noise pollution, truck traffic). Each of these are well defined topic
in itself and out of scope of this thesis. Therefore, the reader is encouraged to look into published
literature for more detailed information about non-water related externalities.
' (a) Dimmock versus Cabot Engineering: Dimmock is a small town in Pennsylvania and has number of shale wells.
In 2010, a Cabot Oil and Gas water well caught fire. The water was slowly contaminated by the gas escaping allegedly
from the fracturing operations. In 2009, Cabot oil and gas had three chemical water surface spills damaging nearby
ecology. (b) Clearfield versus EOG Resource: Clearfield is a county in Pennsylvania. In 2010, a gas blow out caused
unleashed (hazardous) HF wastewater over the ground. EOG was found responsible for improper water management
practices by the PA state regulators. (c) Hopewell Township versus Range Resources: It is one of the major accidents
where diluted HF wastewater spilled into a small tributary resulting in death of aquatic species
4
Figure 2: Hydraulic fracturing water cycle (Source: EPA)
Within the scope of this thesis, I propose achieving following objectives:
1) Understanding the HF wastewater chemical profile and temporal trends over the shale
formations, by building on publicly available data sources (USGS, state agencies)
2) Outline the regulatory framework for fracturing activities and address the regulatory
conundrum surrounding the environmental implication of fracturing activities
3) Qualitative and quantitative evaluation of operational, environmental and spatial dynamics
governing HF wastewater management
4) Development of a modeling tool coupled with a Geographic Information System (GIS)
based front end which could provide utility to a diverse set of stakeholder (regulators,
operators and public) in planning a sustainable HF wastewater management and
5) Provide recommendations for regulatory framework based on the model results
1.3. Thesis structure
There are three parts in this dissertation: Background, Model development, Analysis and
Recommendation. Section 1, Background includes chapter 2-4, focusing on providing the reader
with fundamental concepts and an overview of the challenges in the management of HF wastewater
in fracturing operations. Chapter 2 begins with an introduction to hydraulic fracturing water cycle,
delineating the role of water in different stages of the overall process. Chapter 3 provides an insight
into the regulatory structures in place for fracturing activities, including a discussion about the
federal versus regional regulatory control of fracturing activities. Chapter 4 quantifies the different
challenges in HF wastewater management followed by an evaluation of the different management
models/tools currently or previously employed at industrial level or academic level.
5
Section 2, Model development, includes chapter 5-6 and focuses on the research methodology,
and model structure. Chapter 5 describes the general framework of an integrated water
management system and its objectives, including the tools and techniques used for determining
the various inputs to the model such as wastewater quantity, wastewater quality, and geographical
location of wastewater treatment plants. One of the critical factors in the modelling framework is
the reliable cost estimation of the wastewater treatment plants. Thus, Chapter 6 describes the
engineering design methodology for a Two-Stage Lime Softening Plant and a Reverse Osmosis
Plant to determine the unit production cost of purified water.
Section 3, Analysis and Recommendation, includes chapter 7-10 and focuses on demonstrating the
utility of the modeling platform and developing policy recommendation based on the model
results. Chapter 7 specifically addresses a shale gas field completion in Johnson County, Texas
and discusses the model application results to this particular field development. Furthermore,
sensitivity of the modeled HF wastewater management plan to different operating parameters such
as influent water quality, wastewater recovery rates, etc. is also presented. Lastly, Chapter 8
discusses the policy implications of the model and provides the reader with potential policy
instruments applicable to fracturing industry and the utility of the developed model in
implementation of these policy instruments. Chapter 9 and 10 summarizes the thesis with scope of
future work.
6
Section 1-Background
7
2. Hydraulic fracturing water cycle
Water is an integral part of the hydraulic fracturing process. Large volumes of water are required
to hydraulic fracture a well, out of which 10-40% of the water is returned to the surface as HF
wastewater 5-8 in the initial first two weeks after the well is fractured. During this period, both
water quality and quantity evolves because of a combination of both chemical and physical
interactions occurring in subsurface environment. Understanding these interactions is of
fundamental importance to the process of developing a system to minimize water-related
environmental externalities associated with hydraulic fracturing. Therefore, this chapter describes
these various interactions driving the modifications in water quality and quantity during the entire
hydraulic fracturing water cycle (see Figure 2).
2.1. Water Acquisition
Estimates of water needed per well have been reported to range anywhere from 3-10 million
gallons, depending on the shale formation characteristics9"'. Typically, water for fracturing
operations is either trucked or piped to a well site and stored in tanks or impoundments prior to
fracturing activities begin at the well pad. Conventionally, water for fracturing operation is
procured from local sources such as groundwater wells and surface water bodies such as rivers,
ponds, and lakes. Access to these water sources is likely to become a constraint for the oil and gas
companies, especially those operating in arid regions, which are facing excessive depletion of
.
water resources, and in areas where water flows and availability follow seasonal variations 1" 2
For instance, in arid states like Texas, hydraulic fracturing operations have resulted in deepening
of the existing water unrest in the region. The map in Figure 3, shows the completed wells in black,
overlaid by bands of reds and yellows, with red showing the areas of highest water stress. As
shown in Figure 3, in 2010, the U.S. drought monitor data showed that the Barnett shale counties
experienced a Drought Intensity (DI) in a range of dry to moderate (DI 00-0 1), which dramatically
intensified to a severe to extreme ( DI 2-3) in 2012. In another instance, Susquehanna River Basin
Commission (SRBC) suspended 37 water withdrawals (for at least 48 hours) for operators in the
Marcellus shale region due to drop in localized stream flow levels in SRBC basin in June 2012'.
5 Press release from SRBC, available at http://www.srbc.net/newsroom/NewsRelease.aspx?NewsReleaselD=89
8
Owing to the unreliability of traditional water sources and risks involved in fracturing wells in arid
regions, operators are transitioning towards alternative water sources, namely, industrial
wastewater (e.g. Acid Mine Drainage Water (AMD), HF wastewater), and brackish6 or saltwater.
Water procurement from these sources is costlier than procuring water from conventional sources,
Inoensity:,
00 Abnorm s;ly 0 y
01 M oderat. Drought
02 govers Dlrugh*t
D3CxtreMeDrought
04 F xoe
fal naought
Tha 1lnought Monitor foreuaja
on road-scale
onditions Locol onditone may very See
acwmpanying tortsummary for forrant
atetements
Intonsitv:
U OAnomiolly U ry
D1 Maderaw Drought
02 Ravam Drought
02 txtrem* D rougm
The nmught Manitor foeuwsm on broad-anale
Local canditone may very Soo
acammpanying hut mummary (foraro4aat
codfltiona
aetemaents,
Figure 3: The maps displays the U.S. drought monitor for Texas. The completed wells are shown in black
dots, overlaid by bands of reds and yellows, with red bands depicting areas of highest water stress.
primarily due to an additional step of pre-conditioning of the water to meet the desired influent
water quality criteria for a particular operator. Depending on the technology and the water quality
requirements of the fracturing treatments, the cost of treatment can be less than $1.00 /bbl and as
high as $5-$6/bb1
3
. These costs do not include transportation costs, which could be large if water
sources are not located in proximity of the well site. Strategically, it is important for the industry
to develop less water-intensive fractures. In the midterm, the ability of the industry to utilize
6 Brackish
water refers water containing 0.5-30 parts per thousand salt, which is 10 times less saltier then seawater
(34.7 parts per thousand)
9
alternative water sources will play a significant role in reducing freshwater demand in fracturing
operations.
2.2. Chemical Mixing
After water is available on site, it is blended with fracture fluid additives (chemical compounds)
to formulate a fracture fluid. After the necessary blending, the fracture fluid is injected in the
wellbore at high pressure to induce fractures in the rock strata. For most water-based fluids, the
additives may comprise of no more than 1% of the fluid by volume. However, given the volume
of fluid used in hydraulic fracturing treatments, 1% additives' concentration on an absolute scale
can represent a very significant volume of chemicals. The function of the additives is to alter the
chemical and physical properties of water (e.g. Viscosity, pH, etc.) required for optimal fluid
performance in the subsurface environment. It is not possible here to document an exhaustive list
of additives, but commonly used additives and their functions are noted in Table 114,15 These
include gelling agents, proppant, breaker, friction reducer, corrosion-inhibitor, scale inhibitor,
biocides, cross-linkers, and clay stabilizers. For a detailed description of these additives, please
refer to Appendix B: Description of fracture fluid additives.
Table 1: Commonly used additives in a fracturing stimulation
Additive Type
Chemical compound
Function
Gelling agent
Guar Gum
Thickens the fluid
particles____________________
Silica or resin coated ceramic
Prevents the induced fractures from collapsing
Breaker
Ammonium Persulfate
Allows a delayed breakdown of the polymer
Friction Reducer
Polyacrylamide/Mineral Oil
Minimizes friction between the fluid and pipe
Corrosion-.
inhibitor
N,N, dimethyl formamide
Prevents the corrosion of a pipe
Scale inhibitor
Ethylene glycol
Prevents scale deposits in the pipe
Biocides
Glutaraldehyde
Eliminates bacteria in the water that produce
Cross-linker
Borate salts
Maintains fluid viscosity as temperature
increases
Iron control
Citric acid
Prevents precipitation of metal oxides
Clay Stabilizer
Potassium chloride
Prevents clay from dissolving in the water
Proppant
____
chains
corrosive byproducts
10
The influent water quality dramatically changes after mixing the additives. Hayes et.al reported
the chemical properties of blended water (influent water with fracture additives) sampled at
different well sites in Marcellus and Barnett16 . The summary of their chemical characteristics are
shown in Table 2 (see Appendix C: HF Wastewater Characterization for detailed chemical
characterization). There is a widespread spatial variability in the water quality parameters of the
tested water samples. Typically, the blended fluid composition is rich in organic and nitrogen
content along with a high concentration of salts. However, formulation of fracture fluids from
heavily contaminated influent water can result in a heavily contaminated blended water quality.
For example, in areas where fracture fluid is formulated using untreated HF wastewater, the
blended fluid can be as saline as seawater (35,000 ppm) and have high oxygen demand and may
also contain toxic compounds. Such a fluid if released into the environment (because of accidental
spills or improper handling) can pose some serious health and safety concerns.
Table 2: Analytical water characterization of influent water and after addition of fracture additives
General chemistry
Influent water
Blended water
Units
Hardness as CaCO3'
18-1,080
26-9,500
mg/L
Total suspended solids 2
<2-24
4-5,290
mg/l
Total dissolved solids 3
35-5,510
221-27,800
mg/l
Total organic carbon 4
1.8-202
5.6-1,260
mg/l
Total Nitrogen 5
<3-56.4
0.28-441
mg/l
1Hardness is chemical analysis parameters measuring the amount of divalent ions in a water sample
2
Total suspended solids includes colloidal particles and any particulate matter
3
Total dissolved salts, which reflects the salinity of HF wastewater by measuring the salt (NaCl) amount in water.
4Total Organic carbon is the amount of carbon present in water bound to organic compounds
5
Total Nitrogen is the sum of both organic and inorganic nitrogen present in the water
For a successful fracturing treatment, it is required that the fracture additives are chemically
compatible with the influent water quality, especially when various types of wastewaters are
utilized to formulate the fracture fluid. In general, fracturing additives are sensitive to scaling ions,
dissolved solids and colloidal particles present in the influent water. For example, polyacrylamide
gels (gelling agent) undergo a phenomenon known as "Syneresis" in the presence of high levels
11
of scaling ions. Syneresis is a process where the polyacrylamide chains excessively hydrolyze
(causing precipitation) in solution to carboxylate polyions, resulting in gel collapse. The degree of
hydrolysis is dependent on pH, temperature and divalent ion concentration 1719. In the absence of
scaling ions, Syneresis occurs at elevated temperatures of approximately 200 F 19. High amounts
of scaling ions result in increased hydrolysis sufficient to precipitate the polymer at even low
temperatures, resulting in inadequate fracture fluid performance' 9 . Salinity of influent water also
retards the stability of additives and limits the performance of fracturing treatment. However,
improvements have been made in additive chemistries, which enable the utilization of brackish
water (5000 ppm dissolved salts) for making the fracture fluid. Influent water rich in suspended
solids could result in pre-mature biological degradation of the polymeric gel, resulting in gel
instability and inefficient performance. Thus, controlling the influent water quality is one of the
critical process requirements. Companies engaged in oil and gas development often maintain
relatively strict criteria for the acceptable quality of influent water used when formulating the
fracture fluids for their wells. These water quality requirements depend upon the type of fracture
additives used in the fracture fluid and will vary across the operators.
An increasing number of states require operators to disclose fracture additives being used, with
Wyoming being the first state to implement it. The exact nature of disclosure and exemption of
the data under this disclosure will depend on the implementing state authority, but a widely
common exemption granted across states under this disclosure is the exclusion of proprietary
additives from the disclosure. Publically reported (limited) disclosure are available either on a
national hydraulic fracturing chemical registry7 , managed by the Ground Water Protection Council
(GWPC) and the Interstate Oil and Gas Compact Commission (OGCC) or the state regulatory
website.
2.3. Water Injection
The blended fluid is injected under high pressure in the wellbore to generate sufficient pressure to
fracture the rocks strata. After the fracture fluid reaches the subsurface environment, a variety of
geochemical interactions takes place that alters the chemistry of the fluid. The geochemical
reactions of prime importance in fracturing process are: (1) mixing of injected fluid with formation
' www.fracfocus.org
12
water, and (2) dissolution of sediments from the rocks into the fracture fluid. Water is present in
all the rock formation as the sediment layers are usually deposited by water. Formation water can
be referred to as the total water content of a hydrocarbon bearing reservoir rock. Formation water
is highly saline (250,000-300,000 ppm or greater) and rich in other ionic species such as calcium,
potassium, barium and strontium etc. The mixing of formation water with the fracture fluid is cited
as the prime source of the high salinity content in the wastewater recovered from a fractured well.
Furthermore, as a result of this mixing reaction, the dissolution of minerals in the fracture fluid
(and hence the HF wastewater) from a rock formation is also facilitated
20-,
making the fracture
fluid (and hence the HF wastewater) rich in in calcium, magnesium, etc. This reaction has
significant implications for the management of the recovered HF wastewater, since the extent of
mineral dissolution in fracture fluid is proportional to the fouling potential of the HF wastewater.
CHH
H
H
H
040
.0-
H
I41
H
64
H
ni
'
H
H
-0
o
H
H
H
H
Figure 4: Degradation of a gel by a breaking agent. The breaker used in this case is ammonium persulfate and the
gel is made from Guar Gum. The degradation mechanism is a free radical degradation reaction and prone to exhibit
reduced free radical activity due to inhibition of the free radicals by the degraded fragments
In addition to geochemical reactions, the interactions between the various fracture additives can
also form compounds that influence the chemical profile of the fracture fluid when it returns to the
surface. For example, Guar gels (thickening agent) are degraded using breakers to minimize
formation permeability reduction from obstruction of formation pores by polymeric film. During
13
this degradation reaction (see Figure 4), the activity of oxidizing breakers is limited by the
polymeric by- products formed during the reactions. As a result, high molecular weight polymeric
compounds are found in recovered HF wastewater, which increase not only its toxicity, but also
makes water prone to biological attack. The combination of the above described interactions plays
a critical role in defining the chemical profile of fracturing wastewater as discussed in the next
section.
2.4. HF Wastewater Recovery
Following a fracturing stimulation, large volumes of polluted water (HF wastewater) flows back
to the surface over the lifetime of the well. Estimates of the fraction of hydraulic fracturing
wastewater recovered vary from geologic formation and range from 10% to 40% of the injected
hydraulic fracturing fluid. As shown in Table 3, in Pennsylvania, for example, reported cumulative
volume of liquid waste generated during hydraulic fracturing operations in different PA counties
ranged between 0.5-3 million barrels for the period of six months in 2013 (PADEP).
Table 3: The reported cumulative wastewater volume in different counties in Pennsylvania.
Counties (top 10 liquid
waste)
Waste type
Liquid (bbl)
Solid (ton)
Washington
3,307,467
97,690
Greene
2,154,551
27,044
Lycoming
1,868,362
103,980
Susquehanna
1,703,058
134,930
Bradford
1,537,807
53,796
Westmoreland
Tioga
1,284,411
822,689
13,920
18,628
Clearfield
766,241
2,015
Fayette
758,895
390
587,335
17,268,126
65,261
727,739
Butler
Statewide total
8 Fracturing wastewater represents collectively flowback and produced water. It does not include drilling wastewater
or any solid waste.
14
A thorough understanding of the chemical and physical composition of HF wastewater is
fundamental to mitigate the environmental impacts that may arise from the mismanagement of the
HF wastewater. The chemical constituents of HF wastewater are highly dependent on various
water-rock interactions, the chemicals used in the fracture fluid and the fluid sampling point during
the water recovery period. No typical chemical profile of HF wastewater exists; however, it can
be expected to contain elevated levels of salts, scaling ions, oil and grease and other organics,
naturally occurring radioactive material (NORM), and derivative compounds of those used as
additives in the originally injected fluid 14,23-25. Table 4 shows an exemplary chemical profile of
HF wastewater from the Marcellus shale region16 . The analytical characterization of HF
wastewater quality parameters is a challenging task and requires insight into how different
chemical interferences can limit the accuracy of conventional testing and analysis methods. For
instance, as shown in Table 4, the Chemical Oxygen Demand (COD) values of HF wastewater
increases with time whereas the Biological Oxygen Demand (BOD) values decreases with time.
Depending on the ratio of COD/BOD, the biodegradability or toxicity of an industrial grade
wastewater is determined. Higher ratios imply the wastewater is toxic in nature and requires
specialized management and treatment procedure for safe disposal. In the case of HF wastewater,
despite high COD and low BOD levels, characterizing HF wastewater as highly toxic can be an
erroneous deduction since large COD values are representative of not only organic pollutants, but
also high concentrations of inorganic oxidizable pollutants, especially chloride 26 . Nevertheless,
the limited scope of the conventional COD testing procedure is ineffective in preventing these
errors. Thus, for improving our understanding of the nature of contaminants in HF wastewater, it
is essential to mask any inorganic species before measuring a HF wastewater parameter.
Despite the analytical challenges in HF wastewater characterization, understanding the interaction
between the different ionic species present in the wastewater is vital from the perspective of its
optimal management. From Table 4 it is seen that the water is mildly acidic with moderate
alkalinity 16. Low alkalinity coupled with high level of hardness observed in water implies that the
majority of hardness is a result of non-carbonate salts of divalent ions such as calcium, barium,
strontium and magnesium. Such non-carbonate scales are difficult to remove and can be very
abrasive to equipment surfaces. Among the non-carbonate scales, BaSO4 is of particular
15
importance as it has the lowest solubility product among sulfate salts of divalent ions and is
amongst the first scale to precipitate in the
Table 4: An example of HF wastewater quality
Parameters
Fracture Fluid
(mgL)
HF wastewater
(mg/L)
Day_5
Day 14
pH
Total
Total
Total
Total
Total
7.2
130
735
226
6.6
138
99
17,700
67,300
62.8
6.2
85.2
209
34,000
120,000
38.7
1,730
<2-2,220
4,870
144
8,530
39.8
Alkalinity
suspended solids
hardness as CaCO 3
Dissolved Solids
Organic carbon
Chemical Oxygen Demand (COD)
Biochemical Oxygen Demand (BOD)
Oil and Gas
-
6.3
ND
Calcium
Barium
Strontium
-
4950
686
1080
ND
ND
ND
Sulfate
-
-_-
_
formation. The HF wastewater is supersaturated with respect to barium with an ionic product of
BaSO 4 being in order of 10-3. This is 100,000 fold higher than the solubility product of barium
sulfate (1.05*10-10 at 25 *C) assuming activities in solution for both ions to be unity
27.
The
apparent activity coefficients are likely much lower than 1 due to possible complexing of barium
with low molecular weight acids such as aliphatic acids, dicarboxylic acids, aromatic acids and
cyclic acids
28,29.
The complexing reactions are more common in low salinity HF wastewaters that
contain high concentrations of organic matter 30 . In addition, barite solubility increases with
temperature, pressure and salinity. These factors substantially increase the dissolution of barium
in HF wastewater 22,3 1. HF wastewater with elevated concentrations of barium also contains
elevated concentrations of strontium and radium in the form of Ba-Sr-Ra complexes 32. These
interactions further decrease the activity of barium in solution and lead to increased leaching of
barium from formation rocks into water.
Suspended solids in HF wastewater are colloidal particles with sizes varying between 1 and 10
microns 3 3 . Some of XRD studies have found barite to be the main constituent of suspended solids
24
in Marcellus region. Analytical characterization of suspended solids in the Daqing oilfields in
China has found that the major suspended solid constituents were organics, iron, and barium. 34
16
The presence of suspended solids in HF wastewater increases its turbidity. The excess water
turbidity acts as a barrier to sunlight and provides favorable conditions for growth of bacteria that
ultimately damages the biological profile of the wastewater.
2.5. HF Wastewater Management
As mentioned earlier that improper management of HF wastewater has resulted in negative
consequences for both surface and ground water resources. In considering how to minimize the
environmental impacts of hydraulic fracturing, effectively managing HF wastewater is obviously
critical, and is not trivial.
Generically speaking, three pathways exist for management of HF wastewater: injection into
dedicated wastewater wells, treatment for surface discharge, and reuse or recycling of wastewater
for use during a subsequent fracture treatment. This final pathway may or may not involve some
form of treatment prior to reuse 1435,36. Injection may not be a feasible management pathway in all
plays due to non-availability of suitable injection wells near to the well site. For instance, in
Pennsylvania, there were only seven operating class II wastewater disposal wells in 2008, whereas
Texas had over 11,000 class II wastewater disposal wells in 2008 25. This means that in PA,
disposal of HF wastewater in dedicated injection wells requires the truck haulers to traverse long
distances to out-of-state disposal wells, often located in Ohio and West Virginia. As such, the cost
of injection can be very significant. For instance, in the Bakken shale play, the cost of deep well
injection ranged from $1-$11 /bbl, out of which transportation costs represent 50-80% of total
injection costs. Considering these cost estimates, assessment of the economic potential of HF
wastewater recycling may seem very attractive for certain regions.
The regional regulatory discharge limits heavily influences the treatment of HF wastewater for
surface discharge. In the early stages of Marcellus Shale development, (2008-09) the majority of
HF wastewater water was transported to domestic wastewater treatment plants (WWTP) for
treatment and dilution followed by the subsequent surface discharge. However, WWTPs are
designed to handle municipal wastewaters and so cannot remove contaminants such as dissolved
salts, barium and other potentially harmful substances present in the HF wastewater 31. As a result,
in Pennsylvania, WWTPs have limited the intake of HF wastewater to remain in compliance to
the discharge limits ". Centralized Wastewater Treatment plants (CWT) are better equipped to
handle HF wastewater pollutants by using advanced treatment technologies. Such facilities are
17
costlier than WWTP's and often not locate in proximity of the shale gas fields. Estimates of the
cost of treating the wastewater to regulatory surface discharge quality are approximately between
$3-$6/bbl 38
With increasing shale gas development, reuse and recycling of HF wastewater has emerged as a
promising option for its management. Maximizing recycling can provide various benefits,
including a reduction in the water intensity of fracturing operations by partially making up the
process water demand and reduction in the impacts associated with trucking large volumes of
water to and from a well site. Nowadays, both mobile (on-site) and offsite configurations are
available for HF wastewater treatment. Examples include Ecologix TM ITS system, Aqua-PureTM
NOMAD, etc. The feasibility of the HF wastewater reuse pathway is dependent on the volume of
water produced over time. Wells producing large volumes early in the water production period are
preferred for reuse due to the logistics involved in storing and transportation HF wastewater and
the relatively lower levels of pollutants seen during the initial water production period. For
example, Barnett, Fayetteville. and Marcellus shale all produce approximately 10-15% of HF
wastewater over a period of two weeks, enabling efficient reuse process, whereas Haynesville
wells produce approximately 5% of HF wastewater over the same period, limiting reuse
opportunities . The technologies incorporated for recycling can range from dilution to the use of
desalination technologies such as Reverse Osmosis, Mechanical Vapor Compression and Multieffect Distillation 39,'40. The use of desalination technologies for treating HF wastewater is an
ongoing development and to ensure large-scale deployment of these technologies in the long term,
it is essential to develop cost -effective system that is adaptable to the wide range of pollutants.
18
3. Regulatory framework around hydraulic fracturing
There is a complex set of federal and state statutes governing the development and production of
shale oil and gas in the United States. Federal statutes applicable to shale activities include the
Clean Water Act (CWA) and Safe Drinking Water Act (SDWA). The enforcement and monitoring
of these statutes primarily fall under state authority supplemented with federal oversight. In
addition to these federal regulations, each state may develop its own distinct framework of
regulating hydraulic fracturing activities in their regions based on geological, economics, and
environmental factors. This section will outline the specifics of the federal and state regulations,
which governs the hydraulic fracturing water cycle followed by a discussion about the challenges
faced by policymakers in regulating fracturing activities.
3.1. Federal Regulations
3.1.1. Clean Water Act (CWA)
The act was enacted in 1972 to protect water resources from sewage and industrial toxic
discharges, and contaminated runoff"'0 . The act regulates the discharge of wastewater, including
HF wastewater, though the National Pollutant Discharge Elimination System (NPDES) permits
program, which requires all treatment facilities that discharge from any point source into surface
water to obtain a NPDES permit 41. Permits can be tailored to individual facilities or cover multiple
facilities within a specific geographic region. The permits have two sets of conditions to be met:
(1) technology-based conditions, which generally apply to all permitted treatment facilities, and
(2) water quality conditions which can be unique to each facility and tailored to local conditions
found in the surface water that receives the treated wastewater (Source: NRDC).
Environmental Protection Agency (EPA) may delegate the primary enforcement of issuing the
permits to the states if the states are able to demonstrate that its regulations are as stringent as the
set by EPA. In order to obtain a permit, "treatment facilities must complete an application that,
among other things, describes (1) the waste that will be discharged, (2) where the discharge will
Runoff is the unfiltered water that reaches streams, lakes, sounds, and oceans by means of flowing across impervious
surfaces (Wikipedia).
10 The Federal Water Pollution Control Act Amendments of 1972, Pub. L. No. 92-500, Sec2,86 Stst. 816, codified as
amended at 33 U.S.C. 1251 et seq. (commonly referred to as the Clean Water Act)
9
19
take place, and (3) the method of treatment" 42 . Once the state or EPA has issued a permit, facilities
must report any discharges, including the amount of each pollutant specified in the permit, to the
permitting authority at least once per year 41
Pursuant to pollution control mandates prescribed in CWA, it forbids the shale gas operators from
discharging the contaminated HF wastewater on-site without a NPDES permit. As a result, there
is a surge in the wastewater volumes received by the (permitted) CWT facilities for treatment.
Some operators started using modular treatment systems, which can treat the wastewater on-site,
thereby reducing the risks of waster spills. However, as these mobile units relocate, they are
required to obtain a new NPDES permit.
Unlike the on-site discharge of HF wastewater, the act exempts the discharge of stormwater runoff from a hydraulic fracturing well site. EPA has delegated the decision to regulate this run-off to
the state. For instance, New York and Pennsylvania have permits that regulate the run-off from
constructing and operations of fractured wells.
3.1.2. Safe Drinking Water Act (SDWA)
The SDWA enacted in 1974 protects public health by preventing the contamination of water
quality and thereby providing clean drinking water. Under the act, EPA sets Maximum
Contaminants Level (MCL's) that may be present in water fit for drinking purposes. Pursuant to
protecting the drinking water quality, it also regulates the placement of wastewater and other fluids
underground through the Underground Injection Control Program (UIC). To implement the UIC
program as mandated by the SDWA, EPA has established six categories of injection wells based
on the type of materials injected in them. For the injection of wastewater produced in hydraulic
fracturing operations, Class-II wells are primarily used. EPA may grant the states the authority for
the UIC program if the state programs are as stringent as the federal statutes. Under this authority,
states have primary responsibility for executing the UIC program for their state, including
permitting, monitoring, and enforcements.
Before authorizing a Class II well, EPA or the authorizing state agency must consider the (1)
location of existing wells and other geographical features in the area, (2) well operator's proposed
operating date, (3) injection fluid's characteristics, (4) injection zone's geological characteristics,
(5) proposed well's construction details, and (6) operator's demonstration of mechanical integrity.
20
A suitable HF wastewater injection location requires that a fault and fracture free zone separate
the underground injection zone from any underground source of drinking water. The wells must
be cased and cemented to prevent fluids moving into or between underground drinking water
sources. Once operational, the well's injection pressure cannot exceed a predetermined maximum
and operators must maintain the well's mechanical integrity or cease injection.
One of the frequently debated questions among policy monks revolves around the equivalence of
hydraulic fracturing technology and underground injection in a technological context. The first
attempt to answer this question came in 2005, the Energy Policy Act, which amended the definition
of "underground injection" to exclude "the underground injection of fluid or propping agents
(other than diesel fuels) pursuant to hydraulic fracturing operations related to oil, gas, or
geothermal production activities" 4,". This exemption means that injecting fracture fluid in
subsurface environment do not require UIC permits under the current SDWA regulations. The key
trigger for this exemption can be traced back to EPA's decision in 90's, to exempt hydraulic
fracturing operations from SDWA because the principle function of fracturing operations is not
the injection of fluid but rather the production of gas "'4. In summary, SDWA regulates hydraulic
fracturing operations in two ways: (1) underground injection of HF wastewater in Class-IT wells
is subjected to the UIC permit requirement and (2) if diesel fuel is used in fracturing fluids,
hydraulic fracturing is regulated under SDWA at the point of injection; while all fracturing
operations using non-diesel based fracturing fluid are exempted from point of injection regulations
under SDWA.
3.2. State Regulations
In the United States, the regulation of oil and natural gas exploration and production has always
been primarily a state matter. Economic motives drove the earliest state government interventions
into oil and gas production. The regulatory mechanism commonly deployed by states in regulating
the fracturing activities is as follows:
1) Command and Control Policies
These policy tools traditionally deployed to address pollution problems. It mandates
specific control technologies or production processes that polluters must use to meet a
pollution mitigation standard. Commonly used control measures are either ambient
standards, emissions standards or technology standards. Ambient standards set the amount
21
of pollutant that can be present within a specific environment; Emission standards set the
limit on the amount of the pollutant release by a particular firm; and technology standards
enforces the polluters to install technologies that they deem cost-effective in reducing the
pollution.
2) Market bases incentives
An alternative approach for mitigation pollution is by creating economic incentives for
polluters to incorporate pollution abatement into production decisions. The benefits of such
approach are that the polluters are motivated to innovate so that they can continuously
reduce their pollution levels.
Both these approaches have their advantages and disadvantages. Command and control prove
effective in cases where the Marginal Abatement Cost Curves (MAC)" are uniform across the
industries. In addition, these policies provide a clear outcome with simple monitoring. The
downside of such policies is that they limit an industry's capability to find a cost-effective solution,
leading to economic inefficiency. Not to mention, it is very costly for regulators to collect
necessary information, and they often have to collect it from the sources that they are regulating
- creating favorable conditions for regulatory capture.
On the other hand, market based approaches are flexible, lower cost alternatives to traditional
command and control policies. These approaches derive their efficiency by exploiting the potential
gains from the difference in relative costs of abatement of pollution. The main disadvantage
associated with economic incentives is that they can be inappropriate for dealing with
environmental issues that pose equity concerns'.
In the context of fracturing operations, the predominant regulatory tool used by different states is
command and control ". The key areas of hydraulic fracturing water cycle regulated by states are:
(1) water procurement and use; (2) disclosure of chemicals used in fracture fluid; and (3)
wastewater disposal requirement. Since, the states are still struggling to adapt to the rapid pace of
shale gas developments, the level of stringency in enforcement of regulations in the above key
areas varies significantly across states. Moreover, each state regulates in a way to achieve implicit
" MAC are a set of options available to an economy to deal with pollution.
12 http://yosemite.epa.gov/EE%5Cepa%5Ceed.nsf/webpages/EconomicIncentives.html
22
balance between the benefits of shale gas activities in their region and the environmental risks
posed by these activities.
3.2.1. Water Procurement and Use
State regulations about the water use in fracturing operations are dependent on the water rights
prevalent in the state. There are two types of water rights systems: (1) Riparian and (2) Doctrine
of prior or first appropriation. Riparian rights is a system of allocation of water for those who
possess land along its path13 . All these landowners (including natural gas operators) can make a
reasonable use of the water flowing adjoining to their land. However, the demands of both
upstream and downstream users of water are weighed equally, which means that if the water levels
are insufficient to meet the needs of the users (both upstream and downstream), the water
withdrawals can be curtailed by the states. Such curtailment of withdrawal might not require a
mediated declaration from states, implying that oil and gas operators may have very little warning
to adjust to any such curtailments. Riparian rights are mostly prevalent in the eastern United States
such as Pennsylvania, New England etc.
In contrast, states following the doctrine of prior appropriation (such as Texas, Colorado) allocate
the water rights based on first come first serve basis- the first party to use water for a beneficial
purpose gets the water right. The first party to withdraw water from the stream is referred to as
"senior" water right owner, whereas all subsequent owners are "junior" water right owner 46. In
cases, when the stream has low water level, it is the junior water right owners who are required to
curtail their water use to make up for the senior water right owners. The states apply this
hierarchical curtailment of withdrawal by grouping the owners according to their seniority and
when the rivers are running low, states issues a blanket order to restrict all withdrawals of water
right owners who claimed their right after a certain year. The states issue the orders spontaneously,
leaving very little response time for oil and gas operators to respond to this sudden limitation. The
situation can be complex, especially when the oil and gas companies use a junior water right.
13 http://en.wikipedia.org/wiki/Riparian-waterjrights
23
3.2.2. Fracture Fluid Chemical Disclosure
As mentioned earlier, many states now require oil and gas companies to disclose their fracture
fluid formulations (excluding the proprietary additives) on fracfocus.org. Reports estimate that
130 companies have disclosed the chemicals used in more than 15,000 wells
46.
According to a
survey done by the Resource for Future (RFF) in 2013, 14 states have mandatory fracturing fluid
disclosure requirements (see Figure 5). Every chemical data sheet contains information about the
commercial names of the additives, chemical supplier, CAS number, and chemical concentration
in the fluid. In 2013, nearly 500,000 additives ingredients are listed in the database
Me
"Wo
of
TvAp5
4'.
%undh Uomesynbw of miwd als waft 00O11)
Figure 5: Fracturing Fluid Disclosure requirement across states
3.2.3. Wastewater disposal requirements
Based on the previous discussion about the nature of pollutants and their levels observed in
fracturing wastewater, the release of this wastewater into the environment poses one of the biggest
threats to the environment. The state regulates the storage, trucking and methods of disposal of
wastewater with a varying degree of stringency. Storage of wastewater in some states such as New
York, Michigan require it to be stored in sealed tanks on-site, whereas states such as Ohio allow
for open pits storage of wastewater. In regions storing wastewater in open pits, states regulate the
pit liners for safe isolation of the fracture fluid from the groundwater table. Wyoming is relaxed in
its standards for pit liners and requires liners only "if necessary" to prevent the contamination.45
24
On the other hand, in states like Arkansas pit liner requirement are more stringent, requiring
specifications of pit liners depending on the fluid being stored (fracturing wastewater, drilling
fluid, etc.)
There are also (working) regulations established concerning HF wastewater transport in tanker
trucks. One aspect of these regulations focuses on minimizing the road wear and traffic congestion
resulting from the large number of trucking trips. These include regulations about hours of travel,
selection of truck routes, road use surtaxes. The other aspect of regulations focuses on placing
proper safeguard against any wastewater spills, thereby leading to release of wastewater into the
environment. Either the well operators or the wastewater trucking companies are responsible in
some states to monitor and track the information about the wastewater transported.
3.3. Regulatory conundrum
Shale gas extraction has played an important role in boosting ailing economies by providing
employment opportunities in the states. A study by IHS Global Insight (2009) estimated that
natural gas industry attributes approximately 2.8 million jobs in 2008, amongst which more than
600,000 jobs were "directly involved in exploring, producing, transporting, and delivering natural
gas to consumers or in providing critical supplies or on-site services to the natural gas industry."
There are other spillover benefits of the gas extraction such as increased growth of central water
treatment facilities, booming truck business, and state revenue generation by providing injection
wells for waste disposal. Leaving aside the risk posed by fracturing activities, each state has an
incentive to reap some of these above benefits. States would be willing to relax their regulatory
framework around shale gas extraction, thereby making their state more attractive to operators for
shale gas activities. In this phenomenon, also referred to as "race to bottom", states deregulates the
business environment or taxes in order to attract or retain economic activity in their jurisdictions,
resulting in lower wages, and fewer environmental protections. Not only state regulations but also
federal regulations also exhibit race to bottom phenomenon in its policy framing. Example include
exemption of injection of fracturing fluid from the Safe Drinking Water Act. The race to bottom
effect become disastrous when the risk of water contamination associated with the shale plays is
included in the equation.
Owing to this biased balancing of trade-offs by state, some environmentalist demand centralized
control of fracturing operations. This means that states are no longer the workhorse of federal
25
authorities and the federal agencies regulate every aspect of fracturing operations. Having federal
jurisdiction will provide a uniform regulatory framework structure across states, thereby
empowering states in effective dealing with the environmental risks of fracturing.
However, on the other hand, the inherent widespread heterogeneity in the political, hydrological,
and geological characteristics of the states, often derided as a weakness, is actually a strength; each
state can rapidly respond to its unique blend of economic and political framework to implement a
regulatory structure that caters to their regional demands. Furthermore, state control can address
the distinctive challenges in a timely manner by providing resources instantaneously as and when
needed.
In summary, the current regulatory strategies are more or less a series of tradeoff in protecting the
environment and reaping the benefits of the shale gas activities. These trade-offs inform our
choices about the regulations, but the question arises whether there is a systematic way to address
these trade-offs.
26
4. Challenges in managing HF wastewater
The management of HF wastewater is a complex techno-economic issue. Operators choose
different approaches depending upon a plethora of factors, including the relative economics of
management pathways, and the local availability of water. These factors are exogenous in nature
and their complexity will depend on the various endogenous factors influencing HF wastewater
management. These endogenous factors include temporal and spatial variability, operational
specifications and regulatory environment within the region. In order to develop a solution space
for the optimal management of HF wastewater, this section focuses on laying out these endogenous
factors.
4.1. Temporal and spatial constraints
The volume of wastewater produced declines over time as seen in Figure 6. The bulk of the HF
wastewater produced in the early days, is relatively clean and more suitable for direct reuse by
simply blending. However, the level of contamination of HF wastewater increases with time
making it difficult to reuse by simple dilution techniques. More sophisticated treatments are
required to condition the highly contaminated to enable further reuse or the wastewater is disposed
of
into
injection
well.
This
time-sensitive
nature
of
wastewater
production
of
completion/fracturing operation requires not only flexible logistics support, but also an effective
method to capture these dynamics in making decision about the fate of wastewater.
Range of Water Production Throughout Well Operational Life
P14"a~
no
of ProdsaW
ww
15 days
40
3WL
a
10 to 20
a00rs
0
Figure 6: Temporal trends in wastewater volume produced
27
The temporal dynamics in water quantity and quality are further complicated due to prevalence of
spatial heterogeneity in the location of injection wells, treatment facilities, and well pads. Nonavailability of a management pathway in a close proximity of the well site can result in the
selection of a sub-optimal pathway. For instance, in Texas, underground injection is a preferred
choice of management option for operators not because it is the optimal, but because the injection
wells are available in close proximity to the wells. Because of this heterogeneity, an optimal
wastewater management system requires customization pertinent to the well specific spatial and
temporal dynamics.
4.2. Operational constraints
To achieve a successful fracturing treatment, it is essential that influent water quality is compatible
with the fracture fluid additives. Table 5 summarizes the compatibility issues raised when the
pollutants in the influent water quality are at very high levels. In general, fracturing additives are
sensitive to scaling ions, dissolved solids and colloidal particles present in the water. Mineral
scales tend to precipitate the polymeric gel and result in its collapse. Salinity of HF wastewater
also retards the stability of additives and limits the performance of fracturing treatment. However,
today technical advancements in additive chemistries enable the utilization of brackish water (5000
ppm dissolved salts) for making the fracture fluid. Water rich in suspended solids could result in
pre-mature biological degradation of the polymeric gel, resulting in gel instability and poor
performance. These concerns are elevated, especially in regions using HF wastewater for making
fracture fluids.
Table 5: Summary of potential additive compatibility concerns caused due to presence of high concentration of
pollutant in influent water
Parameters
Total hardness as CaCO 3, mg/l
Concentration
17,700-34,000
Issues
- Inhibits gel hydration and destabilize the gel
- Source of bacteria growth which may result in
Total Suspended Solids, mg/l
100-210
Total Dissolved Solids, mg/l
Chemical Oxygen Demand
(COD), mg/l
Biochemical Oxygen Demand
(BOD), mg/l
67,300-120,000
4,870-8530
biological degradation of polymeric gel
- Damage proppant pack, reducing reservoir
permeability
40-144
4
- Destabilize the gel and increases fouling potential
- Increased accumulation of non-biodegradable
compounds in environment
- High BOD content can damage the biological
profile of water
28
Total Alkalinity (mg/L of
CaCO3)
NORM, pCi/L
85-138
2460-18,000
- Could delay crosslinking of fluids
- High NORM content is hazardous for environment
These concerns make it necessary to control the influent water quality for making the fracture
fluid. Depending on the fracture fluid formulation, it might be the case that the total HF wastewater
simply reused by blending it with fresh water or, the reuse of wastewater is only possible if it pretreated for pollutants. Alternatively, underground injection would be a technically feasible option
provided that the wastewater quality is degraded to such an extent making it not fit for any
treatment technologies or requires excessive freshwater for reaching the targeted influent water
quality criteria.
4.3. Regulatory constraints
Regardless of the fragmented regulatory framework in different states, they play a crucial role in
constraining (or relaxing) the availability of a management option in a region. For instance, in
Pennsylvania, according to the Pennsylvania Code, Title 25, Environmental Protection, Chapter
95 Wastewater Treatment Requirements (PA DEP, 2010) the official effluent standards (daily
maximums) for hydraulic fracturing wastewater as of January 1, 2011 are:
- 500 mg/L for TDS
- 250 mg/L for sulfates
- 250 mg/L for chlorides
- 10 mg/L for total barium
- 10 mg/L for total strontium
In lieu of the observed HF wastewater contamination levels, complying with such stringent
effluent discharge water quality regulatory limits could be difficult task. There are desalination
technologies, which treat the wastewater to these limits, but they are not very cost-effective under
the current scenario. As a result, in Pennsylvania, surface discharge is one of the least commonly
used wastewater management pathways by fracturing operators. In another instance, the Ohio
Department of Natural Resources placed a moratorium on injections into Class II wells in the
Youngstown after finding a "compelling argument" that injections in the wells had caused a series
of earthquakes in 2011 and 2012 41. A consequence of such regulations has indirectly increased
the attractiveness of recycling and reusing pathways in managing HF wastewater.
29
4.4. Evaluation of wastewater management frameworks
Widely formulated frameworks target to capture multiple dimensions affecting wastewater
management. Some noteworthy formulations include the Integrated Multi-Criteria Decision
Making model, Produced Water Management Information System (PWMIS), and Water Decision
tree. These approaches rely on the common principle of formulating an integrated HF wastewater
management system, but seldom do any of them provide a standalone solution to the problem.
Furthermore, they merely re-iterate the complexities and challenges involved in the integration of
diverse sets of interests in managing HF wastewater.
4.4.1. Integrated multi-criteria decision
Integrated multi-criteria decision making models are based on analytical hierarchy processes
(AHP) which integrates subjective and relative preferences in performing analysis of feasible
options 48. To begin with, the first step in the process involves arranging the HF wastewater
management options and the evaluation criteria's in a hierarchical structure. In order to develop a
judgment matrix from the hierarchical structure, pairwise comparison between any two criterions
and assigned a numeric value using a (subjective) scale. The options are assigned scores based on
the comparison matrix and further ranked according to scores. The major advantages of the
approach include a user-friendly interface, the inclusion of verbal and expert judgment, and the
structured analysis of the problem. Despite being a broad spectrum approach, the aggregation of
scores which are from scales of different units is often not easily interpretable
4.
4.4.2. Produced Water Management Information System
Produced Water Management Information System (PWMIS) is an online resource for technical
and regulatory information for managing the produced water. Originally developed by Argonne
National Laboratory for Department of Energy, the system compiles the practices and technologies
in a three-tier system, namely minimization, recycle/reuse, and disposal. Users provide their data
through a series of questions regarding the well characteristics, regulatory framework, and
company policy. Based on these user inputs, the system advances management options, which
have less environmental impact. Currently, the system is unavailable for application.
4.4.3. Water Decision Tree
Water decision tree was jointly developed by Petroleum Technology Alliance Canada (PTAC),
Schlumberger, and Science and Community Environmental Knowledge (SCEK). The decision-
30
making process screens the viability of various treatment options based on series of operational
and water quality questions. Operational questions include information about the fracturing fluid
treatment type, additive types, bottom hole pressure and temperature, and geology of the
formation, whereas the water quality question includes information about blending ratios, target
water quality, etc. Based on these data, the fate of HF wastewater is determined. There is a limited
flexibility concerning direct integration of regulatory and soft factors in the decision process
31
Section 2- Model Development
32
5. Modeling approach for management of HF wastewater
The previous section described various qualitative and quantitative approaches developed or under
research for management of HF wastewater. All the models merely re-iterate the complexities and
challenges involved in effectively addressing and representing the diverse sets of interests in
managing HF wastewater. This section presents an integrated modeling approach to optimize endto-end water management in a hydraulic fracturing water cycle.
5.1. Model Framework
Figure 7 shows a conceptual layout of the model. The model is a linear optimization-based cost
minimization algorithm. The objective of the model is to determine a comprehensive strategy for
the management of HF wastewater in a sustainable and economical manner. In developing this
strategy, there are various inputs required, such as the spatial distribution of water quality,
availability of water resources etc. The decision variables in the model are the volumetric flows
shipped to different treatment endpoints available in the region, denoted by X1, X 2 , X 3 ... , Xn in
Figure 7. Treatment endpoints here refer to different wastewater management pathways such as
underground injection, primary treatment, desalination etc. Quantitative relationships derived
within each endpoint relate process variables to the overall cost of wastewater management using
engineering design analysis and plant sizing studies.
Treatmen
endpoints
X,
HF Wastewater
Treated HF wastewater at
certain quality
-
Dilition
Model boundary
Figure 7: Conceptual layout of the model framework
There are three cost parameters characterizing each treatment endpoint: (1) transportation costs,
(2) treatment cost and (3) displacement cost. The transportation cost includes labor cost and fuel
cost; treatment cost includes operating and capital cost of the plant and; displacement costs signify
the savings (or expenses in the case where wastewater is lost to underground injection wells)
33
resulting from reduction (increase) in water demand for subsequent fracturing operations owing to
any reuse and recycling of streams. Displacement cost is equivalent to transportation cost of
hauling the water to the well site from the fresh water source. The output water stream in the model
is calibrated at a certain water quality such that it could be further reused in the process. The
framework bounds include constraints set on the intake capacities of endpoints, water withdrawal
for any dilution if necessary, and lastly the mass balances across the model boundary. Finally,
based on these inputs and parameters, the objective of the model is to solve for the optimal
management strategy at a minimum cost to various stakeholders. A general mathematical
formulation of the schematic is as follows
Objective function
n
15
Minimize
t=1
{fy[ai(1 + Ei) + bi - CiEi - dEi]vit) + (ft + d)qt
i=1
s.t
vitei(wit - w) 5 qtw
t = 1,2,3 ... T
(influent water quality constraint)
i=1
Wit = f ( win)
t2
(predictingwastewater quality)
n
(qt +
ti
vit) = Qo
t = 1,2,3 ... T
(supply demand relation)
i=1
Vit = Vt
vit 5 yi
vit
t = 1,2,3 ... T
0
t = 1,2,3 ... T
i = 1,2,3 ... n, t = 1,2,3 ... T
i = 1,2,3 . . n,
(volume balance)
(capacity constraints)
t = 1,2,3 . . T
Where,
ai= cost of transportation to
ith
endpoint, bi=cost of treatment for
ith
endpoint, d=price of water in
the region, ci= cost of transportation of water from surface sources to well site, Ei = treatment
efficiency of ith endpoint, vit=wastewater entering in the ith endpoint at time t, wit =output
wastewater quality parameter for ith endpoint at time t, w=influent water quality criteria, Vt= HF
34
wastewater recovered in time t, yi= intake capacity of ith endpoint, qt= total dilution volume used
at different endpoints, Qo= volume of fracture fluid required at a well pad
5.2. Techniques and Methodology
This section outlines the data sources of the different inputs to the models and modeling
methodology.
5.2.1. Determination of HF wastewater quality
As described earlier that one of the challenges in managing the HF wastewater is the temporal
variation in its quality. To resolve the temporally varying wastewater quality, spatial interpolation
methods were employed to predict the water quality in a region.
The workflow for interpolating the water quality is shown in the Figure 8. As the first step, the
USGS Produced Water Database is queried for region of interest. The database provides detailed
information on the water chemistry, sample location, sampling date, completion date etc. This data
is reflective of the ultimate wastewater quality, defined as wastewater quality that has reached a
plateau with respect to time (a common trend observed in HF wastewater as discussed in section
Temporal and spatial).
was watuapie Il
C
C)
ode
neplto
Data mininl
AACIS tolba
Figure 8: Workflow for predicting the wastewater quality in fracturing operations
The queried database is mapped in ArcGISTM to determine the sample distribution on a spatial
scale. The next step is to remove any statistically insignificant outliers from the database using
Cluster and Outlier Analysis toolbox. The tool assigns each input feature in the database a z-value
that indicates statistical significance of the data point based on randomized null hypothesis. The
output of the Cluster and Outlier Analysis toolbox enters as an input to the Interpolation tool (under
Geo-statistical toolbox) to construct a spatial wastewater quality surface. There are various
methods for spatial interpolation such as IDW, Empirical Bayesian Kriging, Polynomial
Interpolation, Radian basis Functions etc. Given the large sample size, Inverse Distance Weighted
(IDW) is selected for our purpose, as other methods are expensive computationally. IDW predicts
35
a value at a non-sampled location based on the assumption that sample points that are close to one
another are more alike than those that are farther. The interpolation generates a spatially distributes
raster image, which consists of ultimate wastewater quality data for each point which lies in the
area of interest.
After assigning each location a predicted ultimate wastewater quality profile, it is desired to
develop a time series of wastewater quality using this information. Based on the literature, the rate
of increase in contamination level is faster in the early days of recovery and drops drastically over
time. Using this information, an exponential increase in contaminant level is assumed such that
ultimate wastewater quality is reached in 10 years after the completion of well. Then, the water
quality as a function of time is written as
w(t) = Aebt
Where,
w(t) is wastewater quality parameterof interestsuch as total dissolved solids, calcium etc
A, is a constant, function of the ultimate wastewater quality value
b is decay constant in units of time- 1
The decay constants are generated randomly under the constraint that at t=10 years w(t) is equal
to Ao. The model's methodology for estimation of these water quality trends has a high degree of
uncertainty due to lack of complete detailed water chemistry dataset. Thus, this method is used as
proxy in cases where a detailed information about the temporal and spatial characteristics of the
wastewater is not available.
5.2.2. Determination of HF wastewater quantity
Similar to the water quality temporal trends, there is a gradual decline in the water recovery rates
(volume per unit time) over time owing to factors such as a drop in pressure gradient, and varying
permeability of rock strata with depth. Severin et.al formulated an empirical model to predict the
water recovery rate in shale formation using the Darcy Law 16. Darcy's Law governs the flow of a
fluid in porous media such as rock. Based on this principle, the rate of water production is linearly
co-related to the cumulative water volume recovered over the life of the fractured well. Thus, the
rate of wastewater production can be written as,
v(t) = Bce-ct
36
Where,
V(t) is the rate of wastewater productionin gallons per day
Bis the cummulative wastewater volume equal to total water
recovered per total operation time
c is decay constant in units of time-'
The total water recovered varies across shale formations and could be anywhere between 10-40%
of the injected water volume. Model assumes that fracturing a well requires on an average injection
of 5 million gallons of water (FracFocus database registry). Thereafter, the total water recovered
from a particular well is the product of percent recovery and injected volume. The decay constant
for decline in HF wastewater production is calculated under the constraint that the cumulative
wastewater is recovered in 90 days following the completion of a well.
5.2.3. Field development data
Each operator has a definitive schedule or workflow for execution of a fracturing process. Among
other details, this schedule documents the timeline of the wells fractured and the number of wells
completed over the operational period. The schedule of completion may change according to the
market dynamics or other contingencies prevalent in the region.
For developing a dynamic decision making strategy, completion schedule is an important input to
the model. Users define their completion schedule via the user interface in the model. The
configurations of the model are set to a default completion schedule acquired from Drilling Info
DatabaseTM, which consists of a well completion schedule for different operators in a given region
in the past.
Target influent water quality, is also a user provided data and varies considerably according to the
fracture fluid formulation employed and formation characteristics. Various other user defined
inputs include the type of fracture fluid formulation, volume of fracture fluid, and expected
recovery rate,
5.2.4. Modeling transport network
Increasing transportation costs for delivery and disposal of water is one of the prime concerns of
the operators. The transportation cost is a function of the distance traversed between two points.
Therefore, minimizing the travelling distance between origin and destinations points will
37
subsequently reduce the transportation costs incurred in the process. In accordance with this rule,
optimized transport networks are formulated connecting different origin (0) and destinations (D)
encountered in a fracturing operation. The frequently encountered O-D pairs in the modeling
algorithm are as follows:
1) Shale gas well-to-well distance
2) Shale gas well-to-wastewater treatment plant
3) Shale gas well-to- underground injection well
4) Shale gas well-to-groundwater well
5) Shale gas well-to- surface water source
Built-in toolbox in ArcGISTM- Network Optimization Toolbox- configures the transport routes
between the above plausible O-D pairs. In order to prepare for running the Network Optimization
Toolbox, different origins and destinations along with street network dataset (source: ESRI) are
mapped in ArcGISTM. The spatial information about the gas wells is a user-defined data source,
whereas the spatial information about wastewater treatment plants, groundwater wells, surface
water sources are obtained from the publicly available database maintained by state agencies. After
mapping the origin, destination and network dataset in ArcGISTM, the network analyst toolbox
solves for the shortest travel distance between two points at a given time.
38
6. Preliminary Engineering Design Analysis
Recycling or reusing the wastewater either for subsequent fracturing process or for discharge to a
surface source requires a prior water treatment application. The simplest form of treatment is
dilution. However, depending on the type and amount of contaminant present in the wastewater, a
complex assembly of different water treatment unit processes may be required to achieve the
desired level of treatment. Such an assembly is referred to as wastewater treatment plants. Reliable
cost estimation of these wastewater treatment plants is a critical input to the modeling framework.
Thus, this chapter discusses the methodology for estimating the construction and operating cost
for two different wastewater treatment plants, namely (1) Two Stage Lime Softening Plant and (2)
Desalination Plant.
6.1. Two-Stage Lime Softening Plant
Figure 9 shows a schematic of the treatment plant. The plant configuration effectively removed
scaling ions (calcium, magnesium, etc.), suspended solids and contaminants removable by
filtration. In lime softening plants, the design follows a sequence of three-unit process: rapid
mixing, flocculation, and sedimentation. Nowadays, rather than using different tanks for achieving
these unit operations, Upflow Solids Contact Clarifiers (UFSCC) is used in the process. In the
UFSCC, rapid mix, flocculation, and sedimentation occur in a single unit. These Clarifiers not
only have high clarification efficiency, but also allow for easy sludge removal.
Lime
Wastewater
)
Sod -ash
Media
filtration
2nstage
softening
Ststage
softening
Sludge
Gravity
Dewatered sludge to
Belt Filter Press
landfill
Thickener
Figure 9: Schematic of a two stage lime soda-ash softening plant
39
Treated HF
Wastewater
There are two clarification stages in the process, each utilizing UFSCC unit. Each clarification
process begins with the mixing of the chemicals into the water, followed by agitation, termed as
rapid mixing and concluded with sedimentation. Rapid mixing allows chemicals to react with the
water, and precipitate scaling ions in the form of their insoluble salts. In this first stage of
clarification, lime (calcium hydroxide) removes any carbonate hardness, which is mainly due to
the presence of any carbonate salts of divalent ions while in the second stage of clarification soda
ash removes non-carbonate hardness present due to any salts of divalent ions with anions such as
sulfates etc.
Rapid mixing follows by flocculation, where the colloidal particles settle down simultaneously
with the insoluble divalent ion salts. Sedimentation is the last step in clarification where the
suspended particles are retained in the tank to settle at the bottom under the influence of
gravitational force. The second stage clarified water (or softened water) is sent to a gravity media
filtration to remove any residual finer suspended solids that escaped the sedimentation tanks. The
treated water may still require further dilution to comply with salinity standards for the end use, as
this treatment sequence does not remove any salinity from the wastewater.
Each unit process in the sequence generates a residual sludge that requires safe disposal. The
current configuration for sludge management includes a sequence of dewatering of the sludge prior
to sending it to a landfill site. Sludge is dewatered using a combination of gravity thickener and
belt filter press. Gravity thickeners use gravitational force to dewater the sludge whereas belt filter
press use mechanical force to concentrate the sludge.
The preliminary cost estimate of the wastewater treatment plant is developed by summing the cost
incurred in each unit process. Primarily, these costs include (1) the capital cost, related with the
total investments and (2) the operating costs, related with the maintenances and current expenses.
The percentage cost of the operating costs decrease as the capacity of the plant increases, owing
to economies of scale.
6.1.1. Cost data sources
Environmental Protection Agency (EPA) documents comprehensive information about the
construction and operating cost of various unit process in form of cost curves 50. The cost curves
parameterize unit process cost as a function of the plant capacities or design variables. Later Qasim
40
et.al developed mathematical equations from these cost curves that simplified the cost estimating
procedures. Hence, mathematical equations as proposed by Qasim are used for estimating the
capital and operating costs". According to this proposed method, the capital costs included in this
estimation are manufacturing and electrical equipment, housing, excavation, site work and labor,
piping and valves, and material cost while the operating expenses include materials, energy, and
labor.
The cost equations used for different unit process (UFSCC, gravity media filter and
chemical feeders) of the plant are listed in Table 6, Table 7, and Table 8.
Table 6: General cost equations for UFSCC
[Upflow Solids Contact Clarifier
Construction Cost Equations for basin area X
Basin area
General form = A + BX
<400 m 2
>400
m2
O&M Cost Equation for basin area X
General form=A + BX
b
a
62801.1
416.8
132264.7
244.3
a
b
5.3
8.8
12.4
5967.9
5806.5
5939.8
G = 70
G= 110
G = 150
Table 7: General cost equations for Gravity filter
Gravity Filter
Backwash Pumping Costs for a filter area X
Construction Costs:
General Form: A + BX + CX 2
A
B
C
o & M Costs for basin area X
36000
1254.21
-0.1212
General Form: AXB + C
A
B
C
Gravity Filter Structure Costs for a filter area X
Construction Costs:
73.3
0.75
2200
General Form: AX 8exp(CX)
o
35483.4
0.591
0.000162
A
B
C
& M Costs for a filter area X
41
General Form: AXB + C
A
359.5
B
0.8568
C
8100
Table 8: General cost equations for chemical feeders
Lime & Soda Ash Feed
Capital Cost for lime feed rate X
General Form = A + Bln(X)
A
B
-24950.9
20424.6
Operating Cost for lime feed rate X
General Form = AXB
A
B
866.3
0.515
The cost of chemicals and filtration media is not included in the cost equations provided in Table
8. The cost of lime and soda ash for this estimate is 20 $/ton and 23 $/ton respectively and the cost
of filtration media is assumed to be equal to 699 $/m3 based on commercial vendor pricing.
Over the years, inflation induced changes in commodity pricing significantly affects the estimation
of capital and operating costs. The majority of the cost functions adopted in the model are prepared
based on commodity pricing in 80's. To adjust for inflation to current year, cost indexes are
frequently used. The most commonly used cost indexes are the ENR Construction cost Index, the
Building Cost Index (BCI) and the Producers Prices Received Index (PPRI). It has often been a
customary practice among planners and engineers to use a single index to encompass the variation
in the cost of different component 52. For this analysis, ENR index is used to update capital
expenses, whereas PPRI index to update the operating expenses.
To update the cost of a treatment plant using the cost index, typically a reference base year is
selected, which is assigned a value of 100 5. In this analysis, 1913 is chosen as the base year. The
cost functions adapted in the model utilize pricing based in the year 1978, therefore in reference
to the 1913 estimates, the ENR cost index for the year 1978 is 2776 and for 2012 is 94142.25 and
42
PPRI index is 115 in 1978 and 284.95 in 201214. Using these values, the revised plant cost in the
year 2012 is calculated as follows:
Cost2o12 USD= Cost1978 USD X (CostIndex2o12/Cost Indexi978)1913 base year
In addition to inflation adjustment, the total capital costs are amortized over the useful life period
of the facility using capital recovery factor (CRF), which is defined below:
I(1 + I)n
[(1 + J)n - 1]
Where, I is the interest rate and N is the number of years over which the cost will be amortized.
All capital costs of this work will be spread over a period of 20 years at an interest rate of 10
percent54.
6.1.2. Process design consideration
6
0
0
Figure
100
200
400
300
500
600
Number of wells completed in an years
700
10: Relationship between cost of wastewater treatment and number of wells completed annually
The on-site treatment plant capacity will vary depending on the average wastewater volume
produced during the completion schedule. As a result, the plant treatment cost will be a function
of the total number of wells completed during the operating period. If w is the total number of
wells completed annually in a region, total annual wastewater volume (WV) produced is given by
Vrw, where V is the volume requiredfor fracturinga well (equal to 5 million gallons) and r is the
HF wastewaterrecovery in ayear (equal to 20% on an average). The relationship between w and
14
ENR
indexes
are
obtained
from
Engineering
News
Review,
http://enr.construction.com/economics/default.asp. The PPR index are obtained from Bureau
43
accessible
of Labor Statistics.
at
the unit cost of wastewater treatment (c) is determined by the simulating the plant cost with plant
capacity as a parametric function of w given by= Vrw, assuming plant has an operating factor equal
to 60%. Figure 10 shows that with increasing number of wells developed in the region, the cost of
treating wastewater decreases. Using Figure 10, the functional form of the unit cost of production
is written as:
C = 294.33w-0.
7 87
This relationship is used in the model to estimate the on-site plant treatment cost required for the
projected development plan proposed in a certain region.
6.2. Desalination Plant
Although dilution of wastewater is the simplest approach to handle salinity levels in fracturing
wastewater, but in many cases where fresh water supply is limited, dilution is not always feasible.
Another approach for managing salinity in fracturing wastewater is by use of desalination
technologies. These technologies can effectively remove dissolved salts from the wastewater
resulting in treating water (permeate) of drinking water quality standards (after some pre and post
treatment).
The two commonly used technologies are either membrane based or evaporation based. Membrane
based technologies include Reverse Osmosis (RO) and Electrodialysis Reversal (EDR) and
Evaporation based technologies include Multistage Flash (MSF) and Multi-Effect Distillation
(MED). Evaporation based technologies are mostly suited for treating high salinity brine
wastewaters and/or larger plants because their energy requirements are high and almost
independent of the source of water salinity ". However, they require lesser conditioning of the
feed stream unlike the membrane based technologies and are less susceptible to fouling and
scaling.
For the purpose of this thesis, we will focus on Reverse Osmosis technology (RO), as it is the most
widespread and mature technology in the United States. The RO cost modeling is performed using
Desalination Economic Evaluation Program (DEEPTM) tool, originally developed for the
International Atomic Energy Agency (IAEA) by General Atomics and later expanded as DEEPTM.
The tool is an excel spreadsheet where different technologies can be evaluated for optimal
performance.
44
6.2.1. Reverse Osmosis (RO)
Reverse Osmosis is a membrane-based technology where the feed water passes across a semipermeable RO membrane by application of high pressure on the feed side. As a result, dissolved
salts (nearly 95% to 99%) are left behind in the reject stream. The amount of pressure applied
depends on the feed salinity-more concentrated feeds require more pressure to achieve the desired
level of rejection of salts. Figure 11 shows a basic schematic of the plant.
RO Membrane
-Pump
,
Prmete Water
(Low Concentration of Sats)
Reject Stream
(Higher Concentration than feed water)
Figure 11: Schematic of a basic RO loop (source: Puretec Industrial Water)
It is important to note that extensive pre-treatment or conditioning of the feed stream is required
before it enters the RO module. In the absence of pre-treatment, the membranes can frequently
foul and develop scales on the surface, requiring costly premature cleaning or membrane
replacements. Thus, the feed is treated both mechanically and chemically for contaminants such
as suspended solids, hardness, bacterial activity etc.
6.2.1.1. DEEP
"'
RO model
The RO model in DEEP
TM
utilizes Polyamide membranes with a design average permeate flux
2
13.2 L/m /h (LMI-). The RO plant is designed to produce a permeate containing dissolved salt
concentration of 199 ppm.
From Table 9 the plant is designed for a recovery ratio of 42%.
Recovery ratio is the ratio of the amount of water that is recovered as desalted water (permeate).
It is a critical performance parameter of an RO plant. Too high recovery can lead to problems of
scaling and fouling whereas too low values can generate large residual waste streams. The recovery
ratio's for RO plants are carefully established by taking into consideration feed water chemistry,
and RO pre-treatment. The residual brine is managed by disposing it into a landfill or injecting
into the saltwater disposal wells.
45
Table 9: RO model design parameters in DEEPTM
RO Model
Recovery Ratio
Seawater Flow
42%
240000
m3/d
Reject brine flow
140000
m3/d
Seawater flow
Outlet dissolved solids concentration
Product water quality (before post)
Temperature correction factor
Salinity correction factor
Membrane area factor (over reference)
Pretreatment, pump, piping size increase factor
Design net driving pressure
Approximate inlet osmotic pressure
Approximate outlet osmotic pressure
Average Osmotic Pressure
High head pump pressure rise
2778
60000
199
1.27
0.788
0.73
0.99
27.9
24
41
34.5
65.4
kg/s
ppm
High head pump power
23.7
MW
Seawater pumping power
Booster pump power
Other power
0.6
1.2
1.7
MW
MW
MW
Energy recovery
-13.14
MW
Total Power use
14.00
MW
Specific Power use
3.36
kWh/m3
Maximum design pressure of the membrane
Constant used for recovery ratio calculation
Design average permeate flux
Nominal permeate flux
Polyamide membrane permeability constant
Nominal net driving pressure
Fouling factor
Aggregation of individual ions correction factor
Specific gravity of seawater feed correction factor
Specific gravity of concentrate correction factor
69
0.00115
13.6
27.8
3500
28.2
0.8
1.05
1.02
1.04
bar
-
-
PPM
-
bar
bar
bar
bar
bar
-
1 /(m2h)
1 /(m2h)
-
-
-
-
bar
6.2.1.2. Economic Analysis
The designed capacity of RO plant is equal to 10,000 m3/day and the incoming feed salinity is
calibrated at 35,000 ppm. The operating factor for the plant is 77% and the costs are amortized
over a lifetime of 25 years with a capital recovery factor of 0.1. The total water produced annually
is 3.3 million cubic meters at a salinity of 200 ppm by using a power of one MW. The detailed
financial model of the plant is summarized in the Appendix D: Detailed techno-economic analysis
46
of RO plant. The capital costs of the plant include construction, and contingency costs.
Construction costs are a significant portion of the capital costs, amounting to 88% of the total
capital expenses. The total specific annualized capital costs of the plant equal to 0.34 $/m3.
The operating expenses are composed of two components: energy costs and maintenance costs.
As the RO process requires high pressures, significant energy costs are incurred in operation of
high-pressure pumps. The energy costs amount to 48% of the total operating expenses. The
remaining 52% of the operating cost is attributed to maintenance costs, which are incurred in
membrane cleaning and replacement, and other general management costs. The total operating
cost of the plant is 0.6$/m3. The total water production cost is 0.931 $/m3. For long-term
profitability of the plant, it needs to generate cash flows from the sale of the desalted water. The
selling price of the desalted water is determined by the payback period method. For an RO plant,
typical payback period are 4-5 years 56. Thus, the selling price of water to the oil and gas operators
was determined to be equal to $10/m3, giving a payback of 4.3 years. Refer to Appendix D:
Detailed techno-economic analysis of RO plant for detailed financial analysis and cost breakdown.
47
Section 3- Analysis and Recommendation
48
7. Case Study Description
This section discusses the application of the modeling platform to a shale gas development located
in one of the major shale plays in the United States. The application will not only provide an
understanding of the functioning of the model in a practical setting, but also demonstrate its utility
in managing the hydraulic fracturing water cycle effectively. Although this application is solely
for the purpose of illustration, the results of the model are indicative of the type of conclusions
generated from a comprehensive user data inputs.
7.1. Barnett Shale
Barnett shale is one of the prolific shale plays in the United States, underlying the city of Fort
Worth, Texas. The play is located in the Fort Worth Basin in the north-central Texas and covers
an area of 5,000 square miles1 . The shale formation is composed of sedimentary rocks of
Mississippian age (354-323 million years ago). The core drilling and fracturing activities in this
play are concentrated in areas that include counties like Denton, Johnson, Tarrant, and Wise. In
these core counties, 1484 horizontal wells were completed in 2010. In 2006 there were 925 wells
completed.
Table 10: Shale has field completion schedule.
API Number
County
State
Completion Date
42-251-34007-00
42-251-34075-00
42-251-33891-00
42-251-33904-00
42-251-33828-00
42-251-33954-00
42-251-34092-00
42-251-34019-00
42-251-34018-00
42-251-34020-00
Johnson
Johnson
Johnson
Johnson
Johnson
Johnson
Johnson
Johnson
Johnson
Johnson
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
4/1/2011
4/3/2011
4/16/2011
4/16/2011
4/17/2011
4/18/2011
4/22/2011
4/28/2011
4/28/2011
4/28/2011
For illustration purpose, a gas field development located in Johnson County is selected for analysis
purposes. The field development schedule is composed of fracturing 10 wells at different spatially
located well pads during a planning period of 30 days (see Table 10) (Source: Drilling Info T M ).
"http://www.netl.doe.gov/File%20Library/Research/Oil-Gas/publications/brochures/DOE-NETL-2011-1478Marcellus-Barnett.pdf
49
There is large variability around the quantity of water injected to fracture these wells. The reported
data on the water volume injected for fracturing wells during 2004-2013 in Texas suggests that the
median volume of water injected into a shale gas well was 4.2 million gallons/15899 m3
(interquartile range in gallons, (3.0*106, 5.5* 106)) (source: Frac Focus database). However, for the
modeling purposes, here a value of 5 million gallons (18927 M 3) of injection water per well is
used.
In a reference scenario, oil and gas companies operating in Johnson County and for that matter the
Barnett Shale has predominantly used underground injection for the management of HF
wastewater. Opportunities of recycling HF wastewater in subsequent fracturing operations are not
completely exploited due to constraints on the cost of treatment, fracture chemistry and spatial and
temporal variability in the water quality and quantity. In this illustration, it is intended to show
that, how the use of the model would have made the HF wastewater management and planning
decisions efficient and minimize the associated adverse impacts on the environment.
7.1.1. Water Supply
Reliable water availability is one of the critical requirements in a fracturing operation. In Johnson
County (and for that matter Texas), companies engaged in the shale gas extraction and production
rely on either groundwater or surface water sources to meet their process water demands. In 2005,
it was estimated that Johnson county utilized 530 million gallons of water annually for fracturing
operations, out of which 320 million gallons of water was obtained from groundwater sourceswhich is approximately 60/40 split between groundwater and surface water sources 57 . The
geography of Texas suggests that the amount of surface water decreases towards the south and
west (combination of decrease in precipitation and increase in evaporations). This means that as
the shale play expands southwards and westwards, fraction of groundwater use in fracturing
activities will most likely increase through time (see Figure 12). Within the extent of Barnett shale,
the Trinity and Woodbine aquifers are the primary groundwater sources, whereas the Brazos River
Basin is the primary surface water source meeting the water demands in the Barnett shale. Figure
13 shows the geospatial data obtained for both these water sources from Texas Water Development
Board (TWDB).
Only a subset of these groundwater and surface water sources can serve as viable water supply for
fracturing operations. Depending on the type of water source, screening criteria for these water
50
resources will differ. For this illustration, screening of the groundwater sources is in accordance
to the Groundwater Availability Model (GAM) criterion while the surface water withdrawal
locations are screened based on the historic USGS stream flow data. GAM establishes a minimum
Figure 12: Estimate of the groundwater/surface water split in different shale regions. The base map shows the
outline of major aquifers and major rivers in Texas. SW stands for Surface water source and GW stands for
groundwater sources.
yield for those groundwater wells where water is withdrawn for use in hydraulic fracturing
operations
17.
Based on the model, water wells supplying water to fracturing activities are required
to have a minimum yield equal to 81 gallons per minute (gpm), with no downtime, which translates
into a minimum yield equal to 50 gpm assuming two wells provide water to an operator. There are
only 23 viable groundwater wells in Johnson County, which satisfy this criterion.
Figure 13: Groundwater location and major rivers in Johnson
51
In mobilizing water from these sources to the well sites, optimal road networks are established to
ensure that the movement of the truck fleet between the origin and destinations are smooth and at
a minimal adverse consequence to the community. For the subset of feasible water sources, road
networks are developed based on the methodology described earlier to determine the road distance
between these water sources and gas well site. The networks are shown in Figure 14 and Figure
15. From these figures it can be seen that, the distance between the shale gas wells and surface
water sources varies anywhere from 6-27 miles (standard deviation 7.5 miles) while the distance
between the shale gas wells and the groundwater wells varies anywhere from 3-34 miles (standard
deviation 8.2 miles) . However, many of these water sources might become unavailable subjected
to seasonal variation and regulatory limits.
Figure 14: Surface river withdrawal locations in vicinity to the gas wells
Figure 15: Road network connecting gas wells to groundwater wells
52
7.1.2. HF Wastewater quality and quantity
The source of contamination in the HF wastewater is attributed to various geochemical reactions
occurring in the subsurface environment and the residual fracture additive chemistry. The three
key water quality parameters analyzed in the wastewater are calcium, total dissolved salts, and
turbidity, mainly because high concentrations of these parameters in the wastewater can lead to
destabilization of the fracture fluid additives17-'9.
Generation of temporally distributed HF wastewater quality profiles requires determination of two
parameters: (1) ultimate effective wastewaterquality and (2) growth rate (see section Techniques
and Methodology). Following the model methodology, USGS Produced Water Database is used
to obtain water quality parameters (total dissolved solids and calcium) at water sampling locations
in Texas to develop spatially interpolated salinity and calcium profiles for Texas (see Figure 16
and Figure 17). Based on the interpolation results, Table 11 shows the estimates of the ultimate
effective wastewater quality parameters for each planned well site. However, the wastewater
samples found in the USGS database were not characterized for turbidity16 parameters. Therefore,
to calculate the spatial distribution of turbidity concentration present in the HF wastewater, each
well is spatially assigned a randomly sampled ultimate effective wastewater turbidity parameter
from a Poisson distribution with a mean turbidity equal to 2000 ppm. Of course, this method is an
approximate representation of the spatial and temporal characteristics of the HF wastewater
turbidity parameter, but the model can develop precise profile with the availability of user-defined
data set.
Table 11: Ultimate wastewater quality parameters for selected well sites
Well
A
B
C
D
E
F
G
Ultimate effective salinity,
ppm
157277
157277
123725
157846
123725
159077
159569
Ultimate effective hardness,
ppm
11075
11075
11449
11155
11449
11779
11865
16 Turbidity here is a measure of concentration of suspended
53
Ultimate effective turbidity,
ppm
2053
2053
2070
1954
2020
1913
1949
It is important to emphasize here that in the Barnett shale, calcium is observed to be a major
constituent of hardness causing ions in wastewater 16. Thus, in this particular case, hardness
parameters are synonymous with calcium concentration in the HF wastewater. In addition, some
wells will show similar HF wastewater quality as they are fractured on the same well pad.
The second parameter- growth rate- as previously mentioned is determined based on the
assumption that the wells reach the effective wastewaterquality over the period of 10 years from
the day they start producing wastewater.
Figure 16: Salinity profiles in Texas based on spatial interpolation
44'
Fgo
Figure
-eol
17: Calcium profiles in Texas based on spatial interpolation
54
Temporally distributed total dissolved solids and calcium profiles are assigned to each fractured
well location by fitting both the ultimate effective water quality parameters and growth rate, to an
exponential growth model. The resulting HF wastewater profiles for the completed 10 wells are
shown in Figure 18, Figure 19, and Figure 20. As shown in these figures, salinity of HF wastewater
can be as high as 80,000 ppm, which is twice the salinity of seawater. Likewise, the maximum
hardness and turbidity concentration is equal to 6000 ppm and 800 ppm, respectively, in a month.
Salinity time-series
100 -
80
.
60
-
40
C2
0
U 20
0 -_
1
6
11
16
Time elapsed, days
21
26
Figure 18: Time series of salinity profiles of HF wastewater
Hardness time-series
-
7000
6000
5000
.2 4000
3000
0
-
8 002000
U1000
*
0
1
6
11
16
21
Time elapsed, days
Figure 19: Hardness time-series profile for HF wastewater
55
26
Turbidity time-series
900
-
-
800
E 700
600
.0 500
400
-
S300
0
-
-
U 200
100
0
1
6
11
16
Time elapsed, days
21
26
Figure 20: Turbidity time-series profile for HF wastewater
Similar to HF wastewater quality profile, the volumetric rate of HF wastewater production is
calculated using an exponential decline curve. Two parameters required for fitting the exponential
decay curve are: (1) cumulative HF wastewater volume and (2) decay constants. Cumulative HF
wastewater volume recovered is a function of the injected water volume and HF wastewater
recovery. In Barnett shale, Nicot et.al have reported a wastewater recovery of approximately 60%
after one year of production". Since the model uses the assumption that the cumulative water
volume is reached in 90 days after the well is brought online, 60% annual wastewater recovery is
equivalent to approximately 15% wastewater recovery in 90 days. For this analysis, a slightly
higher recovery value of 20% is utilized.
To account for heterogeneity in the geology of the formations, the wastewater recovery are
randomly sampled from a Poisson distribution with a mean cumulative HF wastewater recovery
volume equal to 1 million gallons (20% of 5 million gallons) in the first two weeks after the
fracturing treatment). Obviously, the HF wastewater volume distribution varies depending on the
geology of the shale play and the user can define these parameters in the model to reflect the
geological and operational conditions of the development. Using the sampled recovery for each
well, the cumulative water recovery is equal to the product of the recovery (in %) and injection
volume.
56
450
400
350
300
250
200
150
100
50
0
0
10
20
30
Time elapsed, days
40
50
Figure 21: Rate of wastewater production in different wells
In order to integrate the cumulative HF wastewater volumes into the model, it is required to
decompose these volumes into a time series of HF wastewater production. As mentioned earlier
that exponential decay curve are better fit for modeling the HF wastewater production. To generate
this curve for each fractured gas well, the decay constants are calculated using the boundary
condition that at t=90 days, the total water recovered is equal to the cumulative HF wastewater
volume. See the detailed calculation for determining the decay constant in Appendix E: Decay
constant for estimating the volumetric rate of production of HF wastewater. This boundary
condition gives the decay constant equal to 0.22 per day. Using the derived decay constant and
estimated cumulative wastewater volumes, the exponential decline curve for volumetric
production rates for HF wastewater is determined and shown in Figure 21.
7.1.3. Influent water quality
Companies engaged in oil and gas development often maintain relatively strict criteria for the
acceptable quality of influent water used when formulating the fracture fluids for their wells. These
specifications depend upon the type of fracture fluid formulations being utilized. The three general
fracture fluid formulations commonly used are (1) Cross-linked fluids, (2) Slickwater fluids, and
(3) Hybrid fluids. Cross-linked fluids comprise of a gelling agent with one or more crosslinking
agent in water; Slickwater fluids are composed of friction reducing agents and (or) low molecular
weight gelling agents in water; and Hybrid fluids, as the name suggests is a combination of crosslinked fluids and slickwater fluids. A recent analysis of U.S. hydraulic fracturing fluid system
57
trends shows that out of the total wells fractured during the third quarter of 2012, 34% used crosslinked fluids, 24% used slickwater fluids, 40% used a hybrid combination of cross-linked and
slickwater fluids and 2-3% other -miscellaneous types of fluid systems41.
Table 12: The influent water quality for formulating fracture fluid used in the model
Parameter
Range
Total dissolved solids, mg/l
9,000-16,000
Turbidity, NTU
0-5
pH
6.5-8
Iron, mg/l
1-10
Chloride, mg/i
5,000-10,000
Potassium, mg/1
100-500
Calcium, mg/l
50-250
Magnesium, mg/l
10-100
Sodium, mg/l
2,000-5,000
Boron, mg/l
0-20
For the purposes of the analysis presented here the influent water quality specifications utilized in
the model were set as shown in Table 12'9. Based on this guideline, any water that has less than
16,000 ppm salinity and has a hardness level of less than 1000 ppm is considered acceptable as
influent for use in the Johnson County fracturing operations under consideration. It will be later
shown that varying this quality can substantially affect the volume of wastewater reused possible
in the subsequent fracturing process.
7.1.4. HF Wastewater Management
Traditionally, numerous UIC underground injection wells located within Johnson County have
been the dominant pathway for managing the HF wastewater. However, owing to water scarcity
in Texas and growing concerns around potential contamination of drinking water supplies from
HF wastewater, approaches to recycling and reusing the HF wastewater are being actively sought.
There is a broad spectrum of water treatment technologies documented in the literature for removal
of a wide variety of contaminants present in the HF wastewater such as Filtration, Oxidation, and
Adsorption for removal of organics; Nanofiltration, Electrodialysis, Lime-softening, and Ion
exchange for removal for removal of scaling ions; Reverse Osmosis, Multi-Stage Flashing, and
Freeze-Thaw for removal of dissolved salts". The selection of the treatment technology in the
model is a user-defined choice based on the characteristics of their specific field development.
58
For this analysis, however, model considers to reduce the concentration of three key contaminants
in HF wastewater prior to recycling it in the process: scaling ions, chlorides and suspended solids.
This is achieved by using one or more of the following methods: (1) On-site dilution and re-use
without any treatment, (2) On-site lime softening followed by dilution and (3) off-site desalination
(Reverse Osmosis) and discharge. Additionally, underground injection to class II wells are also
available for disposal of any HF wastewater as necessary. It should be noted here that on-site
dilution as an option implies that water is being reused for another fracture treatment on either the
pad of origin or another well pad, depending on the field development schedule.
The geospatial dataset for class 1I injection wells in Johnson County is obtained from the Railroad
Commission of Texas (RRC) and the geospatial dataset for desalination facilities is obtained from
the Texas Water Development Board (TWDB). There are 21 active injection wells (for oil and gas
waste disposal) in Johnson and 38 desalination plants across Texas, out of which the majority of
the plants use Reverse Osmosis (RO) technology. Within a 50 miles radius around the wells studied
here, there are 4 desalination plants and 21 injection wells (see Figure 22). Table 13 shows the
distance between wells to their corresponding closest desalination plant and closest injection well.
Table 13: The distance of nearest injection wells and desalination plants from the gas wells
Well
A
B
C
D
E
F
G
Injection Well (miles)
4.0
6.7
6.8
4.9
2.5
4.0
8.9
Desalination Plant (miles)
9.1
9.1
22
7.3
22
25.8
27.4
It should be noted that these distances are static parameters for each well because the injection
plants and desalination plants are stationary. However, in case when HF wastewater is transported
for reuse at another well site, the same well-to-well distance is a function of time. For example, let
us say that at t=2nd day, well A is sending water to well B as it is located in closest vicinity to the
producing well A and is not yet producing wastewater itself according to the fracturing schedule.
However, at t=1 0 th day, the well A is no longer able to send wastewater to well B as it is already
brought online (or fractured). Thus, both the wells instead send their wastewater to a third well C
scheduled for fracturing on t=12th day and located at an optimal distance from both the producing
59
wells, A and B. This inter-transfer of wastewater between the different well pads will later turn
out to be very useful in managing the HF wastewater for an operator.
Figure 22: The figure on left shows the transportation network from gas wells to deep-water injection wells whereas
the figure on the right shows the transportation networks from gas wells to desalination plants (RO).
7.1.5. Economic inputs
As mentioned earlier that each treatment endpoint is characterized by three cost parameters that
significantly depend on the region of operation. Firstly, the transportation costs are incurred in
hauling water in trucks back and forth from the well site and to appropriate endpoints. The trucking
contracts are based on the mileage allowed per (loaded) mile for mobilization of the trucking fleet
to transport the required volume of liquid. The cost per mile is the summation of the personnel and
fuel cost incurred in trucking the fluid between two locations. For this illustration, the detailed
calculation for estimating the cost per mile is shown below:
Assumption:
A vehicle travels an average of 35 miles per hour at 10 miles per gallon
Fuel costs $3.5 per gallon
Calculations
$100 hourly ratefor personnel divided by 35 mikes per hour= $2.85 per mile
10 miles per gallon at $3.5 per gallon = $0.35 per miles
Total = $3.2 per mile
60
The mileage allowance estimated by this method is in the typical range as quoted in the different
invoices and different contractor survey analysis 60. Of course, actual costs can vary significantly
depending on the market for transport services in the region and so actual transport costs are an
important user-defined exogenous input to the model. It is further assumed that the capacity of the
trucks is 2,500 gallons.
The second cost parameter is the displacement cost. Any wastewater volume, which is recycled
into the system, results in a reduction in the same amount of water volume to be transported to the
well site. Thus, these savings are identified in the model in the form of displacement costs and
equivalent to the cost of water otherwise sourced by the operators for making the injection fluid in
the absence of HF wastewater recycling. These costs mainly include transportation cost of hauling
the water to the well site from the water source.
The third cost parameter is the treatment cost associated with each endpoint. Treatment cost varies
depending on the technologies used for treating the HF wastewater. For this analysis, there are
two treatment technologies available: RO plant and on-site lime softening plant. The RO plant
being stationary has a fixed plant capacity and hence a fixed cost of treatment, equivalent to
10$/m3 (see Desalination Plant for detail plant sizing study). However, the on-site plant being
modular in nature, the unit cost of treating HF wastewater is a function of the number of wells
completed in the region annually. From Drilling Info DatabaseTM, number of wells completed in
Johnson County for the year 2011 is 297. Using the functional form of the relationship between
the wells completed and the cost of treatment as shown in Figure 10, the unit cost of water
production is equal to 0.5 $/bbl (3.3 $/m3). Reported costs for disposal via injection wells vary
broadly. A range of between 0.5-1.75 $/bbl has been reported in the literature, though anecdotal
reporting has suggested higher costs in some instances6 16 2 . For modeling purposes here a lower
value of 0.25 $/bbl is used. Again, these are user-defined inputs and can be altered to reflect the
cost profile of any given pathway within any particular market.
7.2. Results
Over the entire planning period of 30 days, shale gas field (10 wells completed) located in Johnson
County, Texas produce approximately 9.2 million gallons (34826 M 3 ) of HF wastewater. Figure
23 shows the optimized aggregate HF water management plan for the field development, where
the x-axis denotes the progression of the well completion schedule and y-axis shows the total
61
volume of HF wastewater produced by completed wells on each day of the planning period. In
comparison to the reference scenario (where the total volume of HF wastewater is sent for
underground injection to class II wells), the modeled scenario suggests following strategies: (1)
46% of the HF wastewater can be captured in form of reuse (by combination of dilution and onsite treatment) for subsequent hydraulic fracturing operations, (2) 50% of the HF wastewater is
disposed to underground injection and (3) 4% of the HF wastewater is discharged to surface water
after treatment at an off-site desalination plant. These strategies taken together, can result in
curbing at least 9% of the hydraulic fracturing water demands in the base case scenario.
6
a Dilution
aOnsite Treatment
5
a Offsite Treatment
Underground Injection
4
24
4/1
4/6 4/11 4116 4/21 4/26
5/1
5/6
Completion date
Figure 23: Water management plan for the field development
It should be noted here that in this illustration, the field development is not continuous, i.e. after
30 days (total operation time) there is no well scheduled to be fractured. However, the completed
wells still produce wastewater after the total operation time. This volume of wastewater is referred
here as residual wastewater. For this illustration, the management of residual wastewater is
actively managed by underground injection to class II wells and off-site desalination, while the
other two on-site management options are rendered inactive due to discontinuity in the field
development. As seen in the Figure 23, the optimal plan for managing the residual wastewater is
by sending the majority of the volume to a class II well for underground injection and partially to
offsite desalination plants. Alternatively, in a case of continuous field development plans, this
residual wastewater (or a portion of it) will be reflected in the form of reused wastewater (by
dilution or on-site treatment) for subsequent fracturing operation, thereby resulting in a higher
62
percentage of recycled HF wastewater. In this particular instance, the HF wastewater quality and
quantity enabled successful reuse of the majority of the HF wastewater by simple on-site dilution.
This may not be the case for field developments where the HF wastewater quality and quantity are
not conducive for capturing majority of reusable volume of wastewater by simply dilution (e.g.
Marcellus shale play). In these regions, alternative treatment methods such as on-site limesoftening, and off-site desalination system have proven more effective.
While the aggregate water management plan offers insights about the overall field level impact of
the fracturing operations on the water logistics, individual wells (or well pads) may have a slightly
different mix of management strategies, depending on their local spatial and temporal parameters.
There are certain key trends that can be drawn based on the individual well management plans. In
the initial days when a few wells are completed, dilution of HF wastewater is a dominant
management option across the producing wells. However, with the increase in the number of
producing wells with time, underground injection in class II wells becomes the dominant HF
wastewater management option. The reason for such a shift in management option is due the
increasing pollution level in the HF wastewater, which makes it economically and environmentally
sub-optimal to reuse HF wastewater in fracturing operations owing to compatibility concerns with
the fracture additives.
The total cost of implementation of the plan includes water acquisition, management and disposal
costs incurred in a hydraulic fracturing field development. The cost incurred in the reference case
is $ 4.7 per well per barrel of water handled on the well site whereas the base case (shown in Figure
23) costs $ 3.8 per well per barrel of water handled on well site, resulting in a 20 % reduction in
water life cycle costs.
Another significant area impacted by the findings of the base case is the environmental footprint
of the fracturing operations. Because of the optimized water management plans, the number of
truck trips and thereby the resulting community disruptions and noise are reduced drastically. In
the reference case scenario, total round trip trucking miles required to acquire fresh water and
manage the HF wastewater are 58,610 per well. Comparing this to the optimized plan of water
management, the total number of round-trip trucking miles is reduced to 42,000 per well, owing
to internal reuse of wastewater, resulting in a 28 % reduction in trucking miles. This reduction in
63
truck traffic also translates into reduced surface disruptions, low noise pollution, and improved
ambient air quality.
7.3. Sensitivity analysis
7.3.1. Influence of influent water quality
Controlling the quality of water used in formulating the fracture fluid is of prime importance in
HF wastewater decision-making process. As mentioned earlier, the additives used in the fracture
fluid have limited tolerance to the amount of contaminants present in the influent water. Depending
on the operators' fracture fluid formulations, one may require the influent water quality equivalent
to fresh water, while some may be relaxed and accept industrial grade wastewater. This analysis
considers three types of influent water quality requirements are considered, namely: (1) Fresh
Water, (2) Brackish Water (total dissolved solids 5000 ppm) and (3) Seawater (total dissolved
solids 35,000ppm). The default water quality used in the base case is shown in Table 12.
Undergmund Injection
a Offisuie Treatment
wOnsit Treatment
a Dilution
10
1=4
0
Freshwater
Brackishwater
Seawater
Figure 24: The aggregate breakdown of the modeled plan for the three influent water qualities.
Figure 24 shows the aggregate breakdown of the modeled plan for each of the three types of
influent water qualities. The y-axis in this figure denotes the total volume of HF wastewater
managed by each treatment endpoint specified in the model during the entire planning period. It is
evident from the Figure 24 that with relaxing the criterion of the acceptable pollution or
contaminant level in the influent water quality, higher percentage of HF wastewater can be reused
in the subsequent process. Moreover, employing a stringent influent water quality for formulating
fracture fluid will necessitate the utilization of off-site desalination treatment plants to capture any
reusable volume of HF wastewater. Underground injection remains a dominant option in the
64
optimized plan, with its proportion decreasing with decreasing stringency in the water quality
requirement.
Seawater
Base Case
Brakishwater
Freshwe"c
3
4
Cost ($#bb)
5
Figure 25: Impact of influent water quality variation in fracturing operation on the water management costs.
The impact of influent water quality on water cycle life cost are shown in Figure 25. The highest
cost is incurred in the fresh water scenario while the lowest is incurred in seawater scenario. These
cost trends are smoothly aligned with the choice of treatment endpoints in different scenarios.
Since in seawater scenario, due to relaxed water quality allowance, the reusable volume of HF
wastewater can be captured by simply dilution, which is also the relatively cheapest option, the
cost of water management is lowest for this scenario amongst all scenarios. However, in the fresh
water scenario, due to stringent water quality allowance, the reusable volume of wastewater is
captured by a mix of dilution, and off-site desalination, thereby driving the cost of water
management to be the highest amongst all the scenarios.
7.3.2. Influence of water availability in the region
In the base case, the water supply for fracturing operations in Johnson is available within a close
perimeter around the field development plan. However, due to the ongoing drought conditions in
Texas, the availability of water in proximity of the plays is certainly questionable. Water hauling
from sources located at very long distances from the plays, will greatly impact the HF wastewater
management as well as the economics of fracturing water supply chain. Thus, it is vital to
understand these impacts so that contingency plans can be prepared and appropriate measures can
adopted to ensure continual fracturing operations at minimal environmental damage.
To estimate these impacts, the model runs three cases, which are as follows:
65
1) Case A- the mean distance between the gas well and fresh water source is 30 miles
2) Case B- the mean distance between the gas well and fresh water source is 40 miles
3) Case C- the mean distance between the gas well and fresh water source is 60 miles
a Dilution
SOnit* tramMent
a Mte Tn~anwnm
Underwtd Injccton
-6
S 2
0
Base case
Case A
Cae B
Case C
Figure 26: Aggregate water management plan
The results of these cases are compared against the base case (see Results), where the mean
distance between the gas well and the fresh water source is 15 miles. The results are shown in
Figure 26 and Figure 27, which shows aggregate management plan for different cases and cost of
implementation of plan respectively. As it is evident from Figure 26, with increasing hauling
distance between gas wells and water sources, the proportion of wastewater being managed by
offsite desalination plant increases. Simultaneously, there is an increase in overall wastewater
volume captured for reuse with increasing distance of water sources from the gas wells. These
trends indicate that any water supply constraint for a fracturing operation will result in increased
reuse and make the desalination plants attractive.
6
4
0
BaseCase
Cus A
Cus B
Case C
Figure 27: Impact on cost of water management
66
While recycling and reuse of wastewater is encouraging step towards curbing the water stress
prevalent in the region, the cost of implementation of such a strategy increases tremendously in
comparison to the base case. In comparison to the base case, case C has 40 % higher cost of
wastewater management plan, which is due to the higher transportation costs and the increased
proportion of offsite desalination endpoint in the management plan. Based on this analysis, it is
crucial from an operator's perspective to foresee these dynamic challenges and realize the value
of wastewater. Similarly, from a regulatory perspective, these insights are valuable to understand
the intrigued dynamics underlying the fast paced shale revolution and long term planning of
infrastructure.
67
8. Policy recommendations
The model framework is designed to represent the hydraulic fracturing water cycle (water
acquisition and HF wastewater management) as an integrated planning and management system
to examine the interdependency of the factors, including hydrology, geochemistry, physical
infrastructure and regulatory paradigm. As a consequence of the model's holistic representation of
the fracturing water cycle, the model can provide regulators a clear line-of- sight to dynamics
involved in shaping an effective regulatory framework around hydraulic fracturing. This chapter
discusses in detail the key policy outcomes derived from the model application.
8.1. Non-uniform policy framework
As it is seen from the case study that each well differs in its characteristics, thereby making it
evident that each region will have its own customized management plan optimized to its specific
features. Incorporating these insights into policymaking, implies that each state should be
empowered to internalize the variability in geochemistry, resources, economics, etc. so that
optimal policy decisions can be made. Such internalization of heterogeneity at the state level means
that there will be a widespread patchwork of hydraulic fracking regulations across states, with each
region operating its optimal management policy, different from the other.
The case study results have provided strong evidence that the heterogeneity across shale plays
places the state regulatory agencies in a strong position to rapidly respond to its unique blend of
regional dynamics. A typical example of such policy making can be encountered in the case where
the different influent water quality target are employed across regions (Figure 24 and Figure 25).
Depending on the type of influent water quality criterion, different water reuse technologies can
present themselves as the Pareto optimal option. In regions where freshwater quality of influent
water is required, a combination of desalination technologies and dilution are effective in
managing the wastewater. However the strategy is different when the influent water quality is
relaxed to the level of seawater, where it is seen that simple dilution is sufficient to capture the
reusable wastewater. This implies that for a particular state regulatory agency if the prominent
influent water quality in their region is fresh water, investing in public infrastructure such as
desalination plants can prove to be a useful long -term solution; while on the other hand, if the
prominent influent water quality in the region is seawater, promoting dilution practices through
effective regulations (e.g. pipelines investments, liability laws etc.) can prove to be a useful long68
term solution. For implementing these statewide policy changes, it's essential that each state
agency is empowered with resources so that they can invest sufficient time and funds in monitoring
the compliance and enforcement of regulations.
8.2. Market based policy approaches
As mentioned earlier, the current landscape of state policy approaches around hydraulic fracturing
operations are conventionally command and control regulations. These approaches typically
enforce implementation of a uniform pollution control strategies, assuming that all the players
have identical marginal abatement curves. However, it is clearly noted from the model application
that the pollution abatement curve is not uniform across operators and rather a function of various
operating, geological, hydrological and economical parameters. Thus, such intrinsic non-uniform
nature of the pollution abatement curve and the consequent tension to further the environmental
aims, makes market based policy instruments a meaningful approach in context of hydraulic
fracturing.
Market based policy instruments can be implemented in the form of price signals, where the state
agencies levy a tax on the amount of HF wastewater pollution generated in hydraulic fracturing
operations in order to create an incentive for pollution abatement. Establishment of this tax rate
will presumably motivate the operators to manage their wastewater more effectively such that the
marginal costs of management are lower that the relevant tax rate. Conversely, if the marginal cost
of management is higher than the state imposed tax rate, then the operators will continue to
ineffectively manage their wastewater. Therefore, setting the tax rates is of course not a trivial
matter.
The modeling platform can be critical to provide vital information to the state agencies about the
cost incurred in managing wastewater and acquiring water on site. For example, as shown in Figure
23, cost incurred in implementing the modeled plan is $3.8 per well per barrel. If the state agencies
require the operator to follow the optimal management plan, they would need to enforce a pollution
tax rate of at least $3.8 dollar per well per barrel. Thus, such precise information can be used by
policymakers to develop targeted interventions aimed to mitigate water pollution from hydraulic
fracturing operations.
69
8.3. Water management planning
Hydraulic fracturing operations have resulted in a variety of environmental and societal adverse
impacts associated with the acquisition, consumption, and management of water in the process.
To minimize these impacts, it is essential to engage both the state agencies and the operators in the
development of water management plans which will be acceptable to the communities neighboring
oil and gas operations.
Based on the modeling paradigm and the best management practices established by API,
framework for a water management plan will include a review of potential water resources and HF
wastewater management opportunities, anticipated volumes of water required for field level
fracturing activities, and other broad spectrum of competing water requirements and constraints
such as: location and timing of water withdrawal, water sources, water transport, fluid handling
and storage requirements, wastewater disposal options and potential recycling. With a robust water
management plan, both the local water planning agencies and operators ensure that oil and gas
operations do not constraint the resource requirement of local communities and comply with all
regulatory requirements.
To ensure that operators adapt and formulate a water management plan, it is necessary that state
authorities pursue regulatory actions necessitating approval of an operators' water management
plans prior to hydraulic fracturing operations. As a result of such progressive policy, operators will
make water management planning their priority and regulatory agencies can evaluate and assess
the impact of the hydraulic fracturing activities on the environment and local communities in a
more informed manner.
70
9. Future work
This study has advanced our understanding of the complexities involved in developing an
integrated planning tool for effectively managing HF wastewater. Based on the findings of this
study, several potential future research areas can be expanded. At least three future areas of
research'identified are as below:
1) Little is known about the mechanism involved in subsurface fracture fluid migration and
the interaction of chemicals in the subsurface environment. It is suggested that these areas
are explored further through experimental studies which will strengthen our understanding
of the technology.
2) The intriguing issue of high contamination of HF wastewater could be explored further to
establish the kinetics and mechanisms of the geochemical reactions. These findings will
provide a closer look into ways to better control these reactions in the first place, thereby
reducing the amount of contamination in the HF wastewater.
3) On the modeling end of the research, it is recommended that a further study is undertaken
to integrate the model into the different state environmental regulatory agencies so that
model can be validated and feedback can be collected about its long term impacts on
mitigating the environmental impacts associated with hydraulic fracturing.
10. Summary
Recent developments in hydraulic fracturing have enabled a dramatic increase in the production
of unconventional oil and gas resources. Contemporary hydraulic fracturing treatments are water
intensive, and require several million gallons of water per well. Environmental externalities
associated with the shale gas development, particularly related to HF wastewater have drawn
significant public and regulatory attention and should be addressed in an appropriate manner for a
continual development of the shale resources.
This dissertation investigates the underlying complexities in managing the water in hydraulic
fracturing operations and used these findings to develop a modeling platform which can be used
as a decision making tool by operators and regulators. The modeling platform can accommodate
temporal and spatial variability in effluent quality and quantity, and optimize the transportation
logistics associated with moving effluent from well sites and acquiring influent water. The system
71
of this nature not only manages the management of the HF wastewater, but also manages the
acquisition of water required for fracturing as it is seen from the exemplar case study in Johnson
County, Texas. Additionally, such as system can also minimize several environmental impacts
associated with contemporary onshore oil and gas exploration and production activities, including
reducing trucking levels through optimized logistics, and reducing demand on freshwater
resources through maximizing effluent reuse.
The findings of the model and its practical application to the case study also suggests several
courses of policy actions for mitigating environmental impacts associated with hydraulic fracturing
operations. Firstly, heterogeneity in shale plays necessitate a dynamic regulatory regime, which is
tailored to the local conditions. This implies that regulatory center of gravity needs to be in the
states rather than implementing a new federal regime. Secondly, states need to acknowledge the
fact that the marginal pollution abatement curve is not uniform in case of hydraulic fracturing
operations. Thus, market based policy instruments such as taxation, should be employed as a
measure to mitigate the water pollution. The model can be used by the states to determine the
feasible tax rate which is above the marginal cost of pollution abatement. And lastly, a key policy
priority for the state regulatory agencies should be to collaborate with operators and develop a
water management plan prior to operations begin. The model provides a comprehensive template
for a water management plan and could be used as a basis to develop tailored customized regional
solutions.
This dissertation has emerged as a reliable indicator of the fact that the intricacies surrounding the
regulatory environment around water sourcing and disposal is getting increasingly complex, and
that it is demanding more integrative water planning. Thus, the modeling paradigm and its finding
presented in this dissertation will serve as the basis for future studies on developing collaborative
frameworks, which not only mitigates the environmental implication related to the water cycle of
fracturing but also air and so forth.
72
References
(1)
U.S. Department of Energy; Council, G. P. Modern Shale Gas development in the United
States: A Primer; 2009.
(2)
An
Introduction to
Shale
Gas. Available at http://www.3legsresources.com/wp-
content/uploads/2013/06/A-guide-to-shale-gas.pdf2011.
(3)
The
Future
of Natural
Gas:
An
interdisciplinary
MIT
study.
Available
at
https://mitei.mit.edu/system/files/NaturalGas_Report.pdf22l11.
(4)
Carter, N. T. Energy 's Water Demand: Trends , Vulnerabilities , and Management.
CongressionalResearch Service Reportfor Congress2010.
(5)
Blauch, M. E.; Myers, R. R.; Lipinski, B. A. SPE 125740 Marcellus Shale Post-Frac
Flowback Waters - Where is All the Salt Coming From and What are the Implications?
Society ofPetroleum Engineers2009, 1-20.
(6)
Gregory, K. B.; Vidic, R. D.; et al. Water Management Challenges Associated with the
Production of Shale Gas by Hydraulic Fracturing. Elements 2011, 7, 181-186.
(7)
Barbot, E.; Vidic, N. S.; Gregory, K. B.; Vidic, R. D. Spatial and temporal correlation of
water quality parameters of produced waters from devonian-age shale following hydraulic
fracturing. EnvironmentalScience & Technology 2013, 47, 2562-2569.
(8)
Haluszczak, L. 0.; Rose, A. W.; Kump, L. R. Geochemical evaluation of flowback brine
from Marcellus gas wells in Pennsylvania, USA. Applied Goechemistry 2013, 28, 55-61.
(9)
BP International Ltd. Water in the energy industry: An introduction; Williams, E.;
Simmons, J., Eds.; First edit.; 2013.
(10)
Nicot, J.-P.; Scanlon, B. R. Water use for Shale-gas production in Texas, U.S.
EnvironmentalScience & Technology 2012, 46, 3 580-35 86.
(11)
Entrekin, S.; Evans-White, M.; Johnson, B.; Hagenbuch, E.; et al. Rapid expansion of
natural gas development poses a threat to surface waters. Frontiers in Ecology and the
Environment 2011, 9, 503-511.
(12)
Olmstead, S. M.; Muehlenbachs, L. A.; Shih, J.-S.; Chu, Z.; Krupnick, A. J. Shale gas
development impacts on surface water quality in Pennsylvania. Proceedingsofthe National
Academy of Sciences of the United States ofAmerica 2013, 110, 4962-4967.
(13)
An Integrated Framework for Treatment and Management of Produced Water. Report by
Colorado
School
of
Mines,
73
Available
at
http://aqwatec.mines.edu/producedwater/treat/docs/TechAssessmentPWTreatment_T
ech.pdf2009.
(14)
Supplemental Generic Environmental Impact Statement on the Oil, Gas and Solution
Mining Regulatory Program;New York State Department of Environment Conservation
(NYSDEC): New York, 2011.
(15)
Economides, M. J.; Martin, T. Modern Fracturing;ET Publishing, Houston, TX, 2007.
(16)
Hayes, T.; Severin, B. F. Barnett and Appalachian Shale Water Management and Reuse
Technologies. Project report by Gas Technology Institute for Research Partnershipto
Secure Energyfor America (RPSEA) 2012.
(17)
Kabir, A. H. SPE 72119 Chemical Water & Gas Shutoff Technology - An Overview.
Society ofPetroleum Engineers2001.
(18)
DiGiacomo, P. M.; Schramm, C. M. SPE 11787 Mechanism of Polyacrylamide Gel
Syneresis Determined by C-13 NMR. Proceedings of SPE Oilfield and Geothermal
Chemistry Symposium, Denver, CO 1983.
(19)
Moradi-Araghi, A.; Doe, P. SPE 13033 Hydrolysis and Precipitation of Polyacrylamides in
Hard Brines at Elevated Temperatures. Society ofPetroleum Engineers 1987.
(20)
Mathews, R.; Carlberg, B. Solubility of Calcium Sulfate in Brine. Society of Petroleum
Engineering 1973.
(21)
Shi, W.; Kan, A. T.; Fan, C.; Tomson, M. B. Solubility of Barite up to 250 'C and 1500 bar
in up to 6 m NaCl Solution. Industrial& EngineeringChemistry Research2012, 51, 3119-
3128.
(22)
Templeton, C. C. Solubility of Barium Sulfate in Sodium Chloride Solutions from 25 to 95
C. Journalof Chemical and EngineeringData 1960, 5, 514-516.
(23)
Kimball, R. J. Key Considerations for Frac Flowback / Produced Water Reuse and
Treatment. In Proceedings of New Jersey Water Environment Assciation Conference,
Atlantic City; 2012.
(24)
Minnich, K. A Water Chemistry Perspective on Flowback Reuse with Several Case Studies.
In ProceedingsofEPA HydraulicFracturingStudy Technical Workshop on Water Resource
Management; 2011.
74
(25)
Gregory, K. B.; Vidic, R. D.; Dzombak, D. A.; et al. Water Management Challenges
Associated with the Production of Shale Gas by Hydraulic Fracturing. ELEMENTS 2011,
7, 181-186.
(26)
Wolford, R.; Dempsey, B. Characterization of organics in the Marcellus shale flowback and
produced waters, Department of Civil and Environmental Engineering, The Pennsylvania
State University, 2011.
(27)
Haarberg, T.; Selm, I.; et al. SPE 19449 Scale Formation in Reservoir and Production
Equipment During Oil Recovery: An Equilibrium Model. Society ofPetroleum Engineers
1990.
(28)
Neff, J.; Theodor, S. Barium in Produced Water- Fate and Effects in the Marine
Environment. American Petroleum Institute 1995.
(29)
Palmer, A. D.; Drummond, S. E. Thermal decarboxylation of acetate. Part II. Boundary
conditions for the role of acetate in the primary migration of natural gas and the
transportation of metals in hydrothermal systems. Geochimica et CosmochimicaActa 1986,
50, 825-833.
(30)
Shock, E. L.; Koretsky, C. M. Metal-organic complexes in geochemical processes:
Calculation of standard partial molal thermodynamic properties of aqueous acetate
complexes at high pressures and temperatures. Geochimica et Cosmochimica Acta 1993,
59, 1497-1532.
(31)
Blount, C. W. Barite solubilities and thermodynamic quantities up to 300 C and 1400 bars.
American Mineralogist1977, 62, 942-957.
(32)
Hanor, H. S.; Lui-Heung Chan. Non-conservative behavior of Barium during mixing of
Mississippi river and Gulf of Mexico Water. Earthand PlanetaryScience Letters 1977, 37,
242-250.
(33)
Ziemkiewicz, P.; Hause, J.; et al. Zero Discharge Water Management for Horizontal Shale
Gas Well Development. Project report by West Virginia Water Research Institute to U.S.
Departmentof EnergyAvailable at http://www.netl. doe.gov/File%20Library/Research/Oil2012.
Gas/Natural%2OGas/shale%20gas/FEOOO466_TSA.pdf
(34)
Deng, S.; Yu, G.; et al.; Shubo, D.; Gang, Y.; Zhongxi, C.; Di, W.; Fujun, X.; Neng, J.
Characterization of suspended solids in produced water in Daqing oilfield. Colloids and
Surfaces A: PhysicochemicalandEngineeringAspects 2009, 332, 63-69.
75
(35)
Mantell, M. Produced Water Reuse and Recycling Challenges and Opportunities Across
Major Shale Plays. In Proceedingsof EPA HydraulicFracturingStudy Technical Workshop
on Water Resource Management, Washington D.C.; 2011.
(36)
Veil, J. Water Management Technologies Used by Marcellus Shale Gas Producers. Project
report by Argonne National Laboratory to U.S. Department of Energy, Available at
http://fracfocus.org/sites/default/files/publications/water_management-inthemarcellus.
pdf2010.
(37)
Ferrar, K. J.; Michanowicz, D. R.; et al. Assessment of Effluent Contaminants from Three
Facitlites Discharging Marcellus Shale Wastewater to Surface Waters in Pennsylvania.
EnviornmentalScience & Technology 2013.
(38)
Slutz, J.; Anderson, J.; Broderick, R.; Homer, P. SPE 157532 Key Shale Gas Water
Management Strategies: An Economic Assessment Tool. Society of Petroleum Engineers
2012.
(39)
Nathan, K.; Weline Rusty; Ken, N. Water Reuse and Recycling in the Oil and Gas Industry:
Devon's Water Management Success. In Proceedings of 2nd Annual Texas Reuse
Conference, Texas; 2012.
(40)
Vasiliu, C. C.; Pierce, D.; Bertrand, K. SPE 157615 Challenging Wastewater Treatment.
Society ofPetroleum Engineers 2012.
(41)
States, U.; Accountability, G. Energy-Water Nexus: Information on the Quantity, Quality
, and Management of Water Produced during Oil and Gas Production. Report by United
States
Government
Accountability
Office,
Availale
at
http://www.gao.gov/assets/590/587522.pdf2012.
(42)
Hansen,
L. R. Transport, Storage and Disposal of Fracking Waste. Available at
http://www.cga.ct.gov/2014/rpt/2014-R-0016.htm 2014.
(43)
Spence, D. Federalism, Regulatory Lags, and the Politica Economy of Energy Production.
University ofPennsylvaniaLaw Review 2013, 161, 431-508.
(44)
Tiemann, M.; Vann, A. Hydraulic Fracturing and Safe Drinking Water Act Regulatory
Issues. CongressionalResearch Service Reportfor Congress 2013.
(45)
Richardson, N.; Gottlieb, M.; Krupnick, A.; Wiseman, H. The State of State Shale Gas
Regulation. Report by Resourcesfor the Future,Available at www. rfforg/shalemaps2013.
76
(46)
Kulander, C. S. Shale oil and gas state regulatory issues and trends. Case Western Reserve
Law Review 2013, 63.
(47)
Robart, C.; Ruegamer, M.; Yang, A. SPE 163875 Analysis of U . S . Hydraulic Fracturing
Fluid System Trends. Society ofPetroleum Engineers 2013.
(48)
Lee, K.; Neff, J. Produced Water; Lee, K.; Neff, J., Eds.; Springer New York: New York,
2011.
(49)
Ishizaka, A.; Labib, A. Analytic Hierarchy Process and Expert Choice: Benefits and
limitations. Operation Research Insight 2009, 22, 201-220.
(50)
Gumerman, R. C.; Culp, R. L.; Hansen, S. P. Estimating water treatment costs.
EnvironmentalProtectionAgency 1979.
(51)
Qasim, S. R.; Lim, S. W.; Motley, E. M.; G., H. K. Estimating Costs for Treatment Plant
Construction. American Water Works Association 1992.
(52)
Clark, R. M. Estimating water treatment costs: Cost curves applicable to 2500 gpd to 1 mgd
Treatment
Available
Plants.
at
http://nepis.epa.gov/Exe/ZyNET exe/300009IH. TXT?ZyActionD=ZyDocument&Client=E
PA&Index=1976+Thru+1980&Docs=&Query=&Time=&EndTime=&SearchMethod=J
&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QField
1979.
Day=&IntQFieldOp=0&ExtQFieldOp
(53)
Remer, D. S.; Mattos, F. B. Cost and scale-up factors, international inflation indexes and
location factors. InternationalJournalofProductionEconomics 2003, 84, 1-16.
(54)
Hackney, J.; Wiesner, M. R. Cost assessment of Produced Water Treatment. Retrievedfrom
1996.
http://wiesner.cee.duke.edu/files/wiesner/Produced%2OWater%2OCosts.pdf
(55)
Nicot, J.-P.; Walden, S.; Greenlee, L.; Els, J. A Desalination Database for Texas. Contract
reportfor Texas Water Water Development Board2006.
(56)
Macharg, J. P. Energy Optimization of Brackish Groundwater Reverse Osmosis
Desalination. Contractreportfor Texas Water Water Development Board 2011.
(57)
Bene, J.; Harden, B.; Griffin, S. W.; Nicot, J.-P. Assessment of Groundwater Use in the
Northern Trinity Aquifer Due To Urban Growth and Barnett Shale Development. Contract
reportfor Texas Water Water Development Board2007.
(58)
Nicot, J.-P. Hydraulic Fracturing and Water Resources: A Texas Study. The Gulf Coast
Association of GeologicalSocities Transactions2013, 63, 359-368.
77
(59)
Shafer, L. SPE 141448 Water Recycling and Purification in the Pinedale Anticline Field:
Results From the Anticline Disposal Project. Society ofPetroleum Engineers 2011.
(60)
Contractor Cost Outine: Energy and Environment Cabinet Division of Waste Management.
report
by
Division
of
Waste
Management,
Kentucky,
Available
at
http://waste.ky.gov/UST/Forms/2OJ%2OForms%20and%2OOutlines/ContractorCostO
utline.pdf2011.
(61)
Stepan, J. D.; Shockey, E. R.; Kurz, A. B.; et.al. Bakken Water Opportunities AssessmentPhase
1. Available at http://www.undeerc.org/bakken/pdfs/FracWaterPhaseIreport.pdf
2010.
(62)
Mccurdy, R. Underground Injection Wells For Produced Water Disposal. In Proceedings
of EPA Hydraulic FracturingStudy Technical Workshop on Water Resource Management;
2011.
(63)
Aqualon. Guar and Guar Derivatives Oil and Gas Field Applications. Retrieved from
http://www.ashland.com/Ashland/Static/Documents/AAFI/PRO_250-61_Guar.pdf
2007.
(64)
Weaver, J.; Gdanski, R.; et al. SPE 80226 Guar Gum Degradation : A Kinetic Study. Society
of PetroleumEngineers 2003.
(65)
Sarwar, M. U.; Cawiezel, K. E.; et al. SPE 140520 Gel Degradation Studies of Oxidative
and Enzyme Breakers to Optimize Breaker Type and Concentration for Effective Break
Profiles at Low and Medium Temperature Ranges. Society ofPetroleumEngineers 2011.
(66)
Rimassa, S. M.; Howard, P.; et al. SPE 141211 Evaluation of an oxidative biocide during
and after a hydraulic fracturing job in the Marcellus shale. Society ofPetroleum Engineers
2011.
(67)
Blair, C. C.; Chang, K. T.; Trybig, D. S. Use of dispersion polymers as friction reducers in
aqueous fracturing fluids. 6787506, 2004.
(68)
Phillips, K. G.; Hunter, W. E. Method for reducing friction loss in a well fracturing process.
4152274, 1979.
78
List of Appendix
79
Appendix A: Major Shale Plays in the United States
Nftw
AOWI
Figure 28: Major shale plays in the U.S.
80
Appendix B: Description of fracture fluid additives
Gelling agents
CH2OH
H
OH
CH 2OH
OH
0
H
CH2
0
H
HH
H
H
H
H
HH
n
Figure 29: Structural form of guar gum
These fluids consist of a water soluble polymer compound to increase the viscosity of the base
fluid at ambient temperature. The earliest water-soluble polymer to be used in water-based fluids
was Guar Gum or Galactomannan. Guar Gum is a naturally occurring polysaccharide composed
of Galactose and Mannose units. Mannose (M) units are joined together via
P, 1,4, - glycosidic
linkages whereas Galactose (G) units are bonded to Mannose via a, 1, 4, - glycosidic linkages 63.
The ratio of M/G is critical to the solubility of the polymer in water. High ratio implies low
solubility in water. For instance, Guar derived from locust bean has low Galactose content and is
sparingly soluble in water
and salinity
63.
'.
The performance of natural guar is sensitive to pH, temperature, shear
Chemically modified guar derivatives such as hydroxypropyl guar (HPG) and
carboxymethyl hydroxypropyl guar (CMHPG) exhibits improved performance and chemical and
thermal stability in fracturing operations. Fluids using low molecular weight Guar and its
derivatives are also referred to as "Linear gels" based fluids.
Breakers
0
No
0-
0
0
.\ 0
NH;
Figure 30: Structural form of ammonium persulfate
81
After proppant particles are successfully placed into the fracture, the gelled fracturing fluid must
be broken down (or degraded). This is necessary as otherwise the gel would reduce the
conductivity of the induced fracture and ultimately reduce gas production. Breaker compounds are
used to achieve this. Ammonium persulfate is a commonly used breaker due to its strong oxidizing
potential. Enzyme based breakers are also used to degrade the gels. Enzymes are selective for a
specific polymer gel and degrade the gel by the same mechanism as chemical breakers. However,
enzymes are not consumed in the reaction like the chemical breakers and can achieve equivalent
performance to their chemical counterparts at lower concentrations
6
. Widespread application of
enzyme breakers is often constrained due to high cost and high sensitivity to process parameters
such as pH and temperature.
Biocide
0
Figure 31: Structural form of Glutaraldehyde
High reservoir temperatures and an abundance of biodegradable polymeric gel means microbial
growth can take place within a fracture fluid. This microbial growth can rapidly degrade the
polymeric gel, and this can result in loss of fluid viscosity being in only a few hours. In addition,
acid producing bacteria and sulfate reducing bacteria can cause localized corrosion in the
equipment. The latter can also be responsible for well souring and iron sulfide precipitation.
Biocides are mixed into the injected fluid to prevent growth of bacteria in the wellbore
environment. Biocides are regulated by the Environmental Protection Agency (EPA) under the
Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA). The most common biocides are
typically either oxidizing or non-oxidizing in nature. Oxidizing biocides are faster than non-
82
oxidizing biocides, but faster activity could result in interaction of biocide with other components
of the fracture fluid which could compromise the fluid stability 66. Non-oxidizing biocides in
contrast, are generally passive to other fluid components, but are intrinsically hazardous and toxic,
thus requiring special handling.
Commonly used biocides include Glutaraldehyde, DBNPA (2, 2, dibromo-3-nitrilopropionamide),
BNPD (bromonitropropane-1, 3 diol), THPS (tetrakishydroxymethylphosphonium sulfate),
Isothiazolone". Most of these biocides function effectively within a wide pH range; however a
few (THPS, DBNPA) may show limited stability at high ph.
Crosslinking agents
KB
H-O\
O-H
H-0
O-H
Figure 32: Structural form of borate salts
The stability of gelled fluids is significantly influenced by high temperature and high shear stress,
resulting in premature thinning of the gel. To offset these viscosity reductions, initially high
polymer loaded gels were utilized to provide sufficient viscosity. However, it was later found that
excess polymer in the fluid can adversely impact the fracture conductivity. An alternative approach
to improve the stability of the gels is by using crosslinking agents. Such gels are also referred to
as "cross-linked gels", and exhibit a stable viscosity profile at elevated temperature and high shear
regime. Salts of metal ions such as borate, zirconium, titanium, and aluminum are commonly used
as crosslinking agents.
-CH I-C-
~~CO
NH2
-
Friction reducers
n
Figure 33: Structure form of Polyacrylamide
83
Partially hydrolyzed Polyacrylamide (PAM) is commonly used a friction reducing agent in a
fracture fluid. The hydration of PAM is a critical factor influencing its effectiveness as a friction
reducer 9 . This rate is sensitive to the contaminants present in the water such as divalent ions, and
chlorides. Over time, various alternative friction reducing formulations have been commercialized,
which have higher salt and ionic tolerance, improved stability at high temperature and easier
handling and storage characteristics. For example, aqueous dispersions containing either organic
solvents or surfactants are fast dissolving to reach the desired activity67 ; copolymers of acrylamide
.
and quaternary salts are effective in fresh water as well as high salinity brines 6 8
84
Appendix C: HF Wastewater Characterization
Table 14: Summary of influent water quality in Barnett shale play
Para
ter
pH
Acidity
otal Alkalinity
HIardness as CaCO3
ITotal Suspended Solids
Turbidlty
Chloride
Total Dissolved Solids
Specific Conductance
Total Kjeldahl Nrog
Ammonia Nitrogen
Nitrate-Nitrite
Nitrite as N
niochemIcaI Oxygen Demand
Chemical Oxygen Demand
Total Organic Cabon (TOC)
D ssoived Organic Carbon
it & Grease (HEM)
Cyanofe. Total
Amenable Cyanide
romide
Fluonde
Total Sulfide
Sulfate
otal Phosphorus
otal Recoverabie Phenolics
Sulite
ethylene Blue Active
ubstances (MBAS)
Rame
Medm
Uot
6.7-7.4
'5-5.5
6.2-88.8
18-1.080
<2 - 24
1,3-33.7
4.1 - 3.000
35- 5,5100
55-10,100
< 3 - 56.4
0.017-20-8
< 0.1 - 3.0
< 0.05 - 4.9
< 2.0 - 110
< 10 -924
1.8 - 202
1.4 - 222
Not Detected
< 10 -625
< 0.01 - 0.27
< 0.2- 31.9
< 0.05 - 1.2
1.6- 5.6
3.8-139
<0.1 - 0.14
0.01 - 0.031
7.2
No Units
NC
52.5
132
NC
37
35.2
334
423
NC
0.41
NC
NC
NC
NC
3.4
3.2
NC
NC
NC
NC
NC
NC
26
NC
NC
mg/L
6 -21.6
10.8
mg/I
< 0,05 - 0,962
NC
mg/L
85
mg/L
mg/IL
mg/IL
NTU
mg/L
mg/L
umhoslcm
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/I
mg/L
ug/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
Table 15: Summary of water quality parameters of a blended fluid in Barnett shale
pH
Acidity
Total Alkalinity
Hardness as CaCO3
Total Suspended Solids
Turbidity
Chloride
Total Dissolved Solids
Specitic Conductance
Total Kjeldahl Nitrogen
Amimonia Nitrogen
NIlrate-NItdte
Nirite as N
Biochemical Oxygen Demand
Chemical Oxygen Demand
Total Organic Carbon (TOC)
Dissolved Organic Carbon
Oil & Grease (HEM)
Cyanide. Total
Amenable Cyanide
Bromide
Fluoride
Total Sulfide
Sulfate
Total Phosphorus
Total Recoverable Phenolics
Suffite
Methylene Blue Active Substances
(MBAS)
P rme
5.2-8.9
Median
< 0.01 -0.77
< 5 - 61.6
7.2
NC
NC
130
NC
249
902
735
726
33,5
5.9
NC
NC
NC
1,730
226
301
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
<0.03 - 0.506
NC
<5-1.230
5-308
26-9.500
4-5.290
2.7-'15
18-10,700
221 - 27,8000
177-34.600
2.3 -400
028-441
0.1-3.1
<0.05 - 5
< 2.0 - 2220
35.3-47400
5.6 - 1,260
5-1.270
4.6-255
3.5- 954
< 0.01- 0.87
< 0.2 - 107
< 0.05 - 58.3
- 3-8.8
2.9-2,920
< 0.1 - 16
86
ME&
No Units
mg&
mg/L
mgfL
mgAl
NTU
mgtL
mgL
Umhos/cm
mgAL
mgfL
mg/L
gIL
mg/L
mg/L
mgA.
mgIL
ug/L
mgAL
mg/L
mg&
mg/L
mg/i
mgAI
mgAI
mgAI
mg/L
Appendix D: Detailed techno-economic analysis of RO plant
41SF
Consruction Cost
lrienwediale loop cost
Backup Heat Source
lnfA@OLzfaN costs
Water plant owners cost
Water plant cuitingency cost
Inberest d4ang Consuction
Toba Capit Costs
Annuaiezd Capita Costs
Sp. Annualzed Cap Costs
-
RO
12
ToWd (AO)
12
-
-
-
-
-
-
-
-
MSF
Energy Cost
Heat cost
Backup heat cost
Electricity cost
Purhased elkcmrinty cost
Total Energy Costs
Operaion andMainmenance Costs
Management cost
Labour cost
Materal cost
Insurance cost
Totm OSM cast
-2
Total Operating Costs
1
1
D
14
Specfic ($Wn3 d)
1,177
-
Shae
80%
0%
0%
-
1
1
1
0
15
i
5%
4%
8%
2%
77
59
124
34
1470
0.34 $m3
RO
Total (M)
SecVi"c (W4i3)
0.8
0.1
1
0.8
0.1
1
0. 4
D.07
1
0.13
0.27
0.5
0.1
0.24
0.05
028
0.04
0.08
0.17
0.02
1
031
2
0.m
Share
0%
0%
40%
8%
48%
0%
7%
14%
28%
4%
52%
3J06 M$
0.931 SIme
Total water cost
0.931 $1m3
-
Total annual cost
Water production cost
Water Transport costs
87
a.
a
.4i
.4i
.4i
.4i
a
.4i
Ii
~
e.
a
a
ap
A4
-
,
88
Eli
~
~ij~
I
a T
'I
U
I
I I 'It S II
I IiI I I"hi I1 I Ia a
S
Appendix E: Decay constant for estimating the volumetric rate of production
of HF wastewater
V, = Cummulative HF wastewater volume, gallons
)
a = decay constant (day-1
V = volume of HF wastewater produced at any time t, gallons
The volume of HF wastewater production is assumed in the model to follow an exponential decay
curve given by
V = V1'(1 - exp(-at))
Equation 1
Applying the boundary condition i.e. at t=O, V= Vo; t=15 days, V=O
-
VO
t=90
V = ft
V(1 - exp(-at))
Using Taylor series expansionfor exponentialfunction, we get
VO=
=0
VO(l -
I - at +
2!
2
2
)
a
t=90
Solving the integral, we get
a = 0.2
Thus the rate ofproduction of HF wastewater is determined by differentiating the equation 1
dV
=
V 0aexp(-at)
89
Appendix F: Cost summary of different Two Stage Lime Softening Plant
For the onsite- lime softening process (shown in Figure 9), the various component costs and design
factors are outlined below:
1) Upflow Solids Contact Clarifier
"
"
*
*
*
Designed basin area of clarifier is 48 m 2
The velocity gradient in the clarifier is maintained at 70 per sec using commercial
standards
Total annual amortized construction cost: $52,577
Total annual operation cost: $15,421
The mass of sludge generated is 66512 kg/day at solids content of 3%
2) Gravity Media Filter
The design basin filtration bed area is 74 m 2
"
The velocity gradient in the clarifier is maintained at 70 per sec using commercial
standards
" Total annual amortized construction cost: $ 395,176
* Total annual operation cost: $69,419
* The mass of sludge generated is 12333 kg/day at a solids content of 6%
3) Chemical Feeder
"
Design chemical feed rates for lime and soda ash are: 3252 kg/hr and 1996 kg/hr
respectively
* Total annualized amortized construction cost: $ 71,104
* Total annual operating expenses: $ 1,364,534
4) Gravity Thickener
*
* Design sludge loading is 15 kg/hr/m2 resulting in basin diameter of 26 m
* Total annualized construction cost: $93,056
* Total annualized operating cost: $ 12,813
* The sludge is thickened to 30% solid content
5) Belt Filter Press
*
*
*
*
The sludge volume is 2.4 m3/hr
Total annualized construction cost: $114058
Total annualized operating cost: $63815
The sludge is dewatered to 70% solid content
90
Appendix H: Linear Optimization Programing, MatlabTM
The optimization program is coded using MatabTM.
I Neha Mehta, MIT
% S.M. Technology and Policy, 2014
% The Matlab script is a linear optimization program for
% management of HF wastewater in hydraulic fracturing operations
%% Reads the GIS data into Matlab
% read the completion schedule of an operator
well=xlsread('F:\Data\InputModelData','Sheetl','Bl:Bl');
dcomp=xlsread('F:\Data\InputModelData','Sheetl','D8:D14');% Number of wells
completed daily
dailycompl=dcomp';
% reads the total planning period
totaloperation=xlsread('F:\Data\InputModelData','Sheetl','B5:B5');
time sub=xlsread('F:\Data\InputModelData','Sheetl','C8:Cl4');
time=timesub';
% reads the total number of injection wells
in a region
injectionwell=xlsread('F:\Data\InputModelData','Sheetl','B2:B2');
% reads the total number of freshwater sources in a region
freshwaterwell=xlsread('F:\Data\InputModelData','Sheetl','B3:B3');
% reads the total number of desalination plants in a region
offsite-plant=xlsread('F:\Data\InputModelData','Sheetl','B4:B4');
%% defining the variables
filenamell='F:\Data\MappingData\welltowell';
filenamel2='F:\Data\MappingData\welltoUI';
filenamel3='F:\Data\MappingData\welltooff';
filenamel4='F:\Data\MappingData\weIltofresh';
filenamel5='F:\Data\HPDIproducedWaterQuality\saltquality';
filenamel6='F:\Data\HPDIproducedWaterQuality\calquality';
columnrangeww='F2:F50';
columnrangeui='F2:F701';% distance between well to ui
columnrangeoff='F2:F267';% distance between well to offsite plant
columnrangewfre='F2:F169';% distance between well to fresh water
columnrangesal='K2:K8';% Effective Ultimate salinity
columnrangecal='K2:K8';% Effective Ultimate salinity
%% Origin-destination distance matirx based on ArcGIS information
% matrix for well to well distance
disww colum=xlsread(filenamell,'Lines',columnrangeww);
disww reshape(diswwcolum,well,well);
% matrix for well to injection well distance
diswelcolum=xlsread(filenamel2,'Lines',columnrangeui);
diswel=reshape(diswelcolum,injectionwell,well)';
% matrix for well to freshwater source distance
91
diswe3_colum=xlsread(filenamel3,'Lines',columnrangeoff);
diswe2=reshape(diswe2_colum,freshwaterwell,well)';
% matrix for well to desalination plant distance
diswe2_colum=xlsread(filenamel4,'Lines',columnrangewfre);
diswe3=reshape (diswe3_colum,offsiteplant,well)';
t% Creating a state
of well matrix system
statewell=zeros(well,well,total operation);
for t2=1:totaloperation
for i2=1:well
for m2=1:well
if(time(m2)<=t2)
statewell(i2,m2,t2)=1;
end
end
end
end
%% Creating matrix for water quality
% Concentration in ppm
% TDS in ppm
meanTDS=30000;
% hardness in ppm at CaCO3
meanHardness=10000;
% Mean suspended solids in ppm
meanSS= 2000;
allocating memory
salt=zeros(well,totaloperation+10);calci=zeros(well,total-operation+10);
susp=zeros(well,total operation+10);
TDSt=zeros(well,well,total_operation+10);
Hardness t=zeros(well,well,total operation+10);
SSt=zeros(well,well,totaloperation+10);
0 temporal profile of wastewater quality
for qt=l:well
salt(qt,1:totaloperation+10)=wsalinity(qt);
calci(qt,1:totaloperation+10)=wscales(qt);
susp(qt,l:total-operation+10)=wturbid(qt);
end
for t4 1=1:total operation+10
for i4 1=1:well
for m4 1=1:well
TDSt(i4_1,m4_1,t4_1)=salt(m4_l,t4_1);
Hardnesst(i4_1,m4_l,t4_1)=calci(m4_1,t4_1);
SSt(i4_1,m4_l,t4_1)=susp(m4_l,t4_1);
end
end
end
% Fitting exponential growth curve to pollutant concentration
% allocating memory
92
salinity=zeros(well,well,total operation);
scales=zeros(well,well,totaloperation);
turbid=zeros(well,well,total operation);
for t5=1:total operation+10
for i5=1:well
for m5=1:well
if(t5>total operation)
salinity(i5,m5,t5)=TDSt(i5,m5,t5-time(m5)+l);
scales(i5,m5,t5)=Hardnesst(i5,m5,t5-time(m5)+l);
turbid(i5,m5,t5)=SSt(i5,m5,t5-time(m5)+l);
else
if(statewell(i5,m5,t5)==l)
if((t5-time(m5)+l)==0)
salinity(i5,m5,t5)=TDS t(i5,m5,t5);
scales(i5,m5,t5)=Hardnesst(i5,m5,t5);
turbid(i5,m5,t5)=SSt(i5,m5,t5);
else
salinity(i5,m5,t5)=TDSt(i5,m5,t5-time(m5)+l);
scales(i5,m5,t5)=Hardnesst(i5,m5,t5-time(m5)+l);
turbid(i5,m5,t5)=SSt(i5,m5,t5-time(m5)+l);
end
end
end
end
end
end
%%
Exporting the temporal wastewater quality profile to excel
% salinity profile
xlswrite('F:\Data\MappingData\WaterQuality.xls',salt, 'Base', 'Bl:AD7');
xlswrite ('F:\Data\MappingData\WaterQuality.xls',
'Salinity'
}, 'Base', 'Al:Al'
'Target salinity
15000
xlswrite('F:\Data\MappingData\WaterQuality.xls',
ppm'I,'Base','A16:A16');
%hardness profile
xlswrite('F:\Data\MappingData\WaterQuality.xls',calci, 'Base', 'B8:AD14');
xlswrite( 'F:\Data\MappingData\WaterQuality.xls', ('Hardness' }, 'Base', 'A8:A8');
%turbidity profile
xlswrite('F:\Data\MappingData\WaterQuality.xls',susp, 'Base',
xlswrite( 'F:\Data\MappingData\WaterQuality.xls', { 'Suspended
Solids'},'Base','A15:A15');
% Providing distance matrix a time base
we4=zeros(7,38,total operation);
for tl=l:total operation
ww(:,:,tl)=disww(:,:);
wel(:,:,tl)=diswel(:,:);
we3(:,:,tl)=diswe2(:,:);
we4(:,:,tl)=diswe3(:,:);
end
Creating matrix for water production
93
'B15:AD21');
% Wastwater recovery in 2 weeks
meanrecovery=20;
meanw=1000000; sd_w=1;
% Water required to fracture the well,
gallons
avgfracvolume=5000000;
% randomized wastewater recovery rates from different wells based on a
% Poisson distribution
rofl=[26
24
14
19
16
23
24];
volumesrecoverable=(avg-fracvolume/100).*rofl.*dailycompl;
% Fitting exponential decay curve to wastewater production
for t3_1=1:total operation+10
for i3_1=1:well
for m3 1=1:well
V(i3_1,m3_1,t3_1)=volumes recoverable(m3_1)*exp(-.2*t3_1)*.2;
end
end
end
dmstate=ones(well,well,total operation+10);
dmstate(:,:,1:totaloperation)=statewell;
produce=zeros(well,well,totaloperation);
for t3=1:total operation+10
for i3=1:well
for m3=1:well
if(dmstate(i3,m3,t3)==1)
if((t3-time(m3)+1)==O)
produce(i3,m3,t3)=V(i3,m3,t3);
else
produce(i3,m3,t3)=V(i3,m3,t3-time(m3)+l);
end
end
end
end
end
% Writing the wastewater volume in excel
xlswrite('F:\Data\MappingData\WaterVolume.xls',volumes recoverable','Sheetl',
'A2:A8');
Screening of management options based on distance
allocation of memory
gaswell=zeros(well,1,total_operation);
ui well=zeros(well,1,totaloperation);
f_water=zeros(well,1,totaloperation);
offwell=zeros(well, 1, totaloperation);
% screening loop
for t6=1:totaloperation
for i6=1:well
if
(statewell(i6,i6,t6)==1)
buf=length(find(statewell(i6,1:well,t6)==l));
if (buf<well)
[gaswell(i6,1,t6),Il]=min(ww(i6, (buf+1):well,t6));
[ui well(i6,1,t6),I2]=min(wel(i6,1:injectionwell,t6));
[fwater(i6,1,t6),13]=min(we3(i6,1:freshwater-well,t6));
94
[offwell(i6,1,t6),I4]=min(we4(i6,1:offsiteplant,t6));
ind (i6, 1, t6) =Il+buf;
ind2 (i6,1,t6)=I2+buf;
ind3 (i6, 1, t6) =I3+buf;
ind4 (i6,1,t6)=I4+buf;
else
[gas well(i6,1,t6), I]=min(ww(i6, (buf):well,t6));
[ui well(i6,1,t6),I2]=min(wel(i6,1:injectionwell,t6));
[fwater(i6,1,t6),I3]=min(we3(i6,1:freshwaterwell,t6));
[offwell(i6,1,t6),I4]=min(we4(i6,1:offsite plant,t6));
ind
ind2
ind3
ind4
(i6, 1,t6)=buf;
(i6, 1, t6) =buf;
(i6, 1, t6) =buf;
(i6, 1, t6) =buf;
end
end
end
end
%% matching the supply-demand of water in accordance to completion schedule
% allocation of memory to variable
samewell=zeros(well,1,totaloperation);
same ui=zeros(injection_well,l,totaloperation);
samef=zeros(freshwaterwell,l,totaloperation);
same off=zeros(offsite plant, 1, totaloperation);
% maching process loop
for t7=1:total operation
for i7=l:well
bufl=length(find(statewell(i7,:,t7)==l));
if (bufl==l)
same well(i7,1,t7)=O;
sameui(i7,1,t7)=O;
same f(i7,1,t7)=O;
sameoff(i7,1,t7)=O;
else
a=indl(i7,1,t7);
b=ind2(i7,1,t7);
c=ind3 (i7, 1,t7);
d=ind4(i7,1,t7);
buf2=find(indl(l:bufl,l,t7)==a);
buf3=find(ind2(1:bufl,l,t7)==b);
buf4=find(ind3(1:bufl,l,t7)==c);
buf5=find(ind2(1:bufl,l,t7)==d);
if (length(buf2)==l)
% more than one producing bt sending to different well
samewell(i7,1,t7)=O;
else
% sends water to same well
samewell(buf2,1,t7)=l;
end
if (length(buf3)==l)
% more than one producing bt sending to different well
same ui(i7,1,t7)=O;
else
95
% sends water to same well
sameui(buf3,l,t7)=l;
end
if (length(buf4)==l)
%more than one producing bt sending to different well
samef(i7,1,t7)=0;
else
% sends water to same well
samef(buf4,1,t7)=1;
end
if (length(buf5)==1)
% more than one producing bt sending to different well
sameoff(i7,1,t7)=0;
else
sends water to same well
sameoff(buf5,1,t7)=1;
end
end
end
end
%%
Chaacteristics of wastewater management options
All plants of one type are assumed at same capacity rather than
% stochastically differeing the capcity for one type of plant
Although, with more comhrehensive data, more accurate estimate
% of each plant's capacity can be done.
% Average injection capacity in gallons
avguicap=100000;
C4(1:injectionwell)=avg ui cap;
% average onsite treatment capacity
avg one cap=293040;
C2(1:well)=avg_one_cap;
% average onsite dilution capacity in gallons
avgdilcap=1788571;
Cl(1:well)=avgdilcap;
% average desalination plant capacity in gallons
avgdesal plant=2000000;
C3(1:offsiteplant)=avg desalplant;
% Water treatment cost estimates in $/gal
p1=0; % Cost of dilution
p2=.012; % Cost of onsite diltuion
p3=.04; % cost of desalination
p4=.006; % Cost of underground injection
% Efficiency of output volume
el=1; % efficiency of dilution
e2=1; % efficiency of onsite dilution% efficiency of underground injection
of offsite
dilution
e3=1; % efficiency
e4=0; % efficiency
of underground injection
% Output water quality from each end point
[On salinity_out(:,:,:),On_HH_out(:,:,:),OnSSout(:,:,:)]=...
onsitequality(salinity, scales,turbid);
[De-salinityout(:,:,:),DeHH-out(:,:,:),DeSS-out(:,:,:)]=...
96
desalquality(salinity,scales,turbid);
Water Cost
waterprice=0; % $/gallon
% Influent water quality maintained by operators
TDSrr=15000;
HH rr=5000;
SSrr=100;
TargetQual=[TDSrr,HH_rr,SSrr];
% allocating memory to variable matrix
al=zeros(1,5);dl=zeros(1,5);
cl=zeros (1,5);
% Objective function for linear optimization
fo=zeros (1,5);
Al=zeros (7,5);
DA=500000;
on=zeros (well, 1);
X_sol=zeros(well,5,total_operation);
% Case when only one well is producing wastewater
for kl=1:total operation
on=length(find(statewell(1,:,kl)==1));
if(on==1)
fo=zeros (1,5);
% inequality matrix
A1=zeros (7,5);
% Inequality co-efficient matrix
bl=zeros(7,1);
% Water quality constraints
% Salinity compatibility
al(1)=el*(salinity(1,1,kl))/DA;
al(2)=e2*(Onsalinity out(1,1,kl))/DA;
al(5)=TDSrr/DA;
% Hardness compatibility
d1(1)=el*(scales(1,1,k1))/DA;
dl(2)= e2*(On_HHout(1,1,kl))/DA;
dl(5)=HHrr/DA;
% Turbidity compatibility
cl(1)= el*(turbid(1,1,kl))/DA;
cl(2)= e2*(OnSSout(1,1,kl))/DA;
cl(5)=SS
rr/DA;
% Optimization constraint formations
A1(1,:)=al(1:5);% salinity balance
Al(2,:)=dl(1:5);% hardness balance
Al(3,:)=cl(1:5);% suspended solids balance
A1(4:7,1:4)=eye(4,on*4); % capacity constraints
for pjl=1:3
bl(pjl,1)=Target_Qual(l,pjl);
end
bl(4:7,1)=[C1(1);C2(1);C3(1);C4(1)1;
Co-efficient of equality matrix
Aeql=zeros(1,5);
97
Aeql(1,1:4)=ones(1,4);
beql (1,1) =produce (1, 1,kl);
% lower bound on decision variables
lb=zeros(5,1);
% Co-efficient of objective function
dill(1)=pl*el+transportation(gaswell(1,1,kl))*e1+...
water price-transportation(fwater(1,1,kl));
on1(1)= p2*e2+transportation(gas well(1,1,kl))*e2+...
water price-transportation(fwater(1,1,kl));
desl(1)= p3*e3+transportation(offwell(1,1,kl))*e3-...
transportation(fwater(1,1,kl))*.42;
uil(t)=p4*(r-e4)+transportation(ui
well(1,1,k))*(1-e4)+...
transportation(f water(1,1,kl));
water1(1)=transportation(f-water(1,1,kl));
% objective function
fo(1:5)=[dill(1), uil(1),onl(1), desl(1), waterl(1)];
% linear programing problem framing
[x2(1:5,1,kl),fvall(1,k1)]=linprog(fo,A1,bl,Aeql,beql,lb, []);
g2=reshape(x2,5,l,kl);
Solution to the optimization problem
X_sol(1,:,kl)=g2(:,1,kl)';
else
% allocating the memory to variables
A3_1=zeros(1,5);A3_2=zeros(1,5);A3_3=zeros(1,5);A3_4=zeros(4,5);
% more than one well producing
% Here the trick is to optimize all wells at same time by use of
% two conditions which automatically handles the concept of water
% being sent to same well
for 11=1:on
well indexing
dew=indl (11,1, k);
dew2=find(indl(1:on,1,kl)==dew);
% all dilution variable
w=reshape(linspace(1,5*length(dew2)4,length(dew2)),length(dew2),1);
all onsite treatment variable
z=reshape(linspace(2,5*length(dew2)3,length(dew2)),length(dew2),1);
% all offsite treatment variable
v=reshape(linspace(3,5*length(dew2)2,length(dew2)),length(dew2),1);
% all injection variable
t=reshape(linspace(4,5*length(dew2)1,length(dew2)),length(dew2),1);
% all fresh water variable
wa=reshape(linspace(5,5*length(dew2),length(dew2)),length(dew2),1);
% more than one well producing but sending to different wells
if (length(dew2)==1)
% Water quality constraints
98
% Salinity compatibility
a3(1,11)=el*(salinity(1,l1,kl))/DA;
a4(1,l1)=e2*(On salinity out(1,11,kl))/DA;
a5(1,11)=TDSrr;
% hardness compatibility
d3(1,1l)=el*(scales(1,11,kl))/DA;
d4(1,l1)=e2*(On_HHout(1,l1,kl))/DA;
d5(1,ll)=HHrr/DA;
% turbidity compatbility
c3(1,11)=el*(turbid(1,11,kl))/DA;
c4(1,11)=e2*(OnSSout(1,ll,kl))/DA;
c5(1,ll)=SSrr;
% Optimization constraint formations
% salinity balance
A3_1(1, [1,2,5])=[a3(1,11),a4(1,11),a5(1,11)];
% hardness balance
A3_2(1, [1,2,5])=[d3(l,l1),d4(1,l1),d5(1,11)];
% turbidity balance
A3_3(1, [1,2,5])=[c3(1,11),c4(1,11),c5(1,11)];
% capacity constraints
A3_4(:,1:4)=eye(4);
A3=vertcat(A3_1,A3_2,A3_3,A3_4);
% CO-efficient matrix
b2(1,1)=TargetQual(1);
b2(2,1)=Target Qual(2);
b2(3,1)=TargetQual(3);
b2(4:7,1)=[Cl(1);C2(1);C3(1);C4(1)];
Aeq2=zeros(1,5);
Aeq2(1,1:4)=ones(1,4);
beq2(1)=produce(1,l1,kl);
lb2=zeros(5,1);
% Co-efficient of objective function
dil2=pl*el+transportation(gaswell(11,1,kl))*el-....
transportation(fwater(ll,1,kl)); % Total cost for
dilution
onsi2= p2*e2+transportation(gas well(ll,1,kl))*e2+...
water_price-transportation(fwater(ll,1,kl)); % Total
cost for dilution
des2= p3*e3+transportation(offwell(ll,1,kl))*e3-...
transportation(fwater(11,1,kl))*.42;
ui2=p4*(1-e4)+transportation(ui well(ll,1,kl))*(1-e4)+...
transportation(fwater(11,1,kl));
water2=transportation(f water(ll,1,kl));
99
objective function
ff1 (1,1) =dil2;
fl(1,2)=onsi2;
fl(1,3)=des2;
ff1 (1,4) =ui2;
f1(1,5)=water2;
% linear optimization
[x1(1:5,1,k1),fval2(1,kl)]=linprog(fl,A3,b2,Aeq2,beq2,lb2,[]);
g4=reshape(xl,5,1,ki);
% optimization solution
X_sol(ll,:,kl)=g4(:,1,kl)';
else
% Case when wells send water to management options located
% at same position
if (on<well)
% allocating memory to variables
a3_1=zeros(1,length(dew2));a4_1=zeros(1,length(dew2));
a5_1=zeros(1,length(dew2));
d3_1=zeros(1,length(dew2));d4_1=zeros(1,length(dew2));
d5 1=zeros(1,length(dew2));
c3_l=zeros(1,iength(dew2));c4 1=zeros(1,length(dew2));
c5_1=zeros(1,length(dew2));
for ml=l:length(dew2)
% Water quality constraints
% Salinity compatibility
a3_1(1,m1)=el*(salinity(1,dew2(ml),kl))/DA;
a4_1(1,m1)=e2*(Onsalinityout(1,dew2(ml),kl))/DA;
a5_1(1,m1)=TDSrr/DA;
% Hardness compatibility
d3_1(1,ml)=el*(scales(1,dew2(ml),kl))/DA;
d4_1(1,ml)= e2*(OnHHout(1,dew2(ml),k1))/DA;
d5_l(1,m1)=HHrr/DA;
% Turbidity compatibility
c3_1(1,m1)= el*(turbid(1,dew2(m1),k1))/DA;
c4_1(1,m1)= e2*(OnSSout(1,dew2(m1),k1))/DA;
c5_1(1,ml)=SSrr/DA;
end
% allocate memory to inequality matrix
A4_1=zeros(1,5*length(dew2));
A4_2=zeros(1,5*iength(dew2));
A4_3=zeros(1,5*iength(dew2));
% Formulating inequality constraints
for pj=1:length(dew2)
% salinity constraints
A4_1(1,w(pj))=a3_1(1,pj);
A4_1(1,z(pj))=a4_1(1,pj);
A4_1(1,wa(pj))=a5_1(1,pj);
% hardness constraints
A4_2(1,w(pj))=d3_1(1,pj);
100
A4_2(1,z(pj))=d4_1(1,pj);
A4_2(l,wa(pj))=d5_1(1,pj);
% turbidity constraints
A4_3(l,w(pj))=c3 1(1,pj);
A4_3(1,z(pj))=c4_1(1,pj);
A4_3(l,wa(pj))=c5_1(1,pj);
end
A4=vertcat(A4_1,A4_2,A4_3);
% Inequality co-efficient matrix
b3(1,1)=TargetQual(1);
b3(2,1)=TargetQual(2);
b3(3,1)=TargetQual(3);
% upper bound of decision variables
ub2=zeros(5*length(dew2),1);
ub2(w(1:length(dew2)),1)=C1(1);
ub2(z(1:length(dew2)),1)=C2(1);
ub2(v(1:length(dew2)),1)=C3(1);
ub2(t(1:length(dew2)),1)=C4(1);
dop=length(dew2)+1;
Aeq3=zeros(dop,5*length(dew2));beq3=zeros(dop,1);
for jj=l:length(dew2)
Aeq3(jj,[w(jj),z(jj),v(jj),t(jj)])=l;
beq3(jj)=produce(l,dew2(jj),kl);
end
% lower bound of decision variables
lb3=zeros(5*length(dew2),1);
% allocation of memory to variables
dil3=zeros (1,length(dew2) );onsi3=zeros (1,length(dew2));
des3=zeros(1,length(dew2));
ui3=zeros (1,length(dew2) ) ;water3=zeros (1,length(dew2));
f2=zeros(1,5*length(dew2));
x3=zeros(5*length(dew2),1,kl);
% Co-efficient of objective function
for kk=l:length(dew2)
dil3(kk)=pl*el+transportation(gas well(dew2(kk),l,kl))*eltransportation(fwater(indl(dew2(kk),l,kl))); % Total cost for dilution
onsi3(kk)=
p2*e2+transportation(gas well(dew2(kk),l,kl))*e2transportation(fwater(indl(dew2(kk),l,kl))); % Total cost
des3(kk)=
p3*e3+transportation(offwell(dew2(kk),l,kl))*e3transportation(f water(indl(dew2(kk),l,kl)))*.42;
ui3 (kk)=p4* (1e4)+transportation(ui well(dew2(kk),l,kl))*(1e4)+transportation(fwater(indl(dew2(kk),l,kl)));
water3(kk)=transportation(fwater(indl(dew2(kk),1,kl)));
end
% Objective function formulation
101
for dilution
f2(1,w(l:length(dew2)))=dil3(:);
f2(1,z(l:length(dew2)))=onsi3(:);
f2(l,v(l:length(dew2)))=des3(:);
f2(1,t(1:length(dew2)))=ui3(:);
f2(1,wa(l:length(dew2)))=water3(:);
[x3(1:5*length(dew2) ,1,kl),fval3(1,kl)]=linprog(f2,A4,b3,Aeq3,beq3,lb3,ub2);
g3=reshape(x3,5,length(dew2),ki);
% Optimal solution
for hi=1:length(dew2)
X sol(dew2(hi),:,kl)=g3(:,hi,kl)';
end
management of residual HF wastewater
else
for lo=1:10
% desalination decision variables
gg=linspace(1,2*length(dew2)-l,length(dew2));
% underground injection decision variables
vv=linspace(2,2*length(dew2),length(dew2));
% upper bound of decision variables
ub3=zeros(2*length(dew2),1);
ub3(gg(1:length(dew2)),1)=C3(l);
ub3(vv(l:length(dew2)),l)=C4(l);
, allocating memory to inequality matirx
Aeq4=zeros(1,2*length(dew2));beq4=zeros(1,1);
% Equality co-efficient matrix
for hp=l:length(dew2)
beq4_4(hp)=produce(1,dew2(hp),kl+lo-1);
end
% equality constraint matrix
for toll=1:length(dew2)
Aeq4(toll,[gg(toll),vv(toll)])=1;
beq4(toll)=(beq4_4(toll));
end
Co-efficient of objective function
for kk=1:length(dew2)
des4(kk)= p3*e3+transportation...
(offwell(dew2(kk),1,k1))*e3-...
transportation(fwater(dew2(kk),1,k1))*.42;
ui4(kk)=p4*(l-e4)+transportation...
(ui well(dew2(kk),1,k1))*(1-e4)+...
transportation(fwater(dew2(kk),l,kl));
end
% objective function
f3(1,gg(1:length(dew2)))=des4(:);
f3(l,vv(l:length(dew2)))=ui4(:);
% lower bound to the variables
lb4=zeros(2*length(dew2),1);
x4=zeros(2*length(dew2),1,kl+lo-1);
% linear
optimization formulation
[x4(1:2*length(dew2),1,kl+lo-1),fval4(1,kl+lo-1)]= ...
102
linprog(f3,[],[],Aeq4,beq4,lb4,ub3);
g6=vertcat(zeros(2,length(dew2),kl+10-1),...
reshape(x4,2,length(dew2),kl+lo-1),...
zeros(1,length(dew2),kl+lo-1));
% optimized solution
for go=1:length(dew2)
X_sol(dew2(go),:,kl+lo-1)=g6(:,go,kl+lo-1)';
end
end
end
end
end
end
end
Writing the optimal solution in excel sheets
configuring the solution data
for oo=l:total operation
Management(oo,1)=sum(Xsol(:,l,oo));
Management(oo,2)=sum(Xsol(:,2,oo));
Management(oo,5)=sum(Xsol(:,5,oo));
end
for lol=1:well
Dilutionw(lol,:)=sum(Xsol(lol,1,1:29));
Onsitew(lol,:)=sum(Xsol(lol,2,1:29));
Offsitew(lol,:)=sum(Xsol(lol,3,1:29));
Injectw(lol,:)=sum(Xsol(lol,4,1:29));
end
for joo=1:total operation+10-1
Management(joo,3)=sum(Xsol(:,3,joo));
Management(joo,4)=sum(Xsol(:,4,joo));
end
prodvolu=zeros(39,1);
prod volu(:,1)=sum(produce(1,:,:));
% Cost of management,$/bbl cost cost
price=sum(fvall(1,:))+sum(fval2(1,:))+sum(fval3(1,:))+(sum(dailycompl)*10^6*5
-sum(Management(:,1))-sum(Management(:,2)))*transportation(15); % Total
dollar cost
unitcost=price*31.5/(sum(dailycompl)*10^6*5);
%% Recording the total trucking mileage
for tlO=l:totaloperation
for ilO=l:well
dmil(ilO,1)=(X sol(ilO,1,tlO)*gas well(ilO,1,tlO)*2/5000);
onmil(ilO,1)=(Xsol(ilO,2,tlO)*gas well(ilO,1,t1O)*2/5000);
ofmil(ilO,1)=(Xsol(ilO,3,tlO)*offwell(ilO,1,tlO)*2/5000);
inmil(ilO,1)=(Xsol(ilO,4,tlO)*gas well(ilO,1,tlO)*2/5000);
fmil(ilO,1)=2000000*f water(ilO,l,tlO)*2/5000;
end
A mile(1,1)=sum(dmil);
A-mile(2,1)=sum(onmil);
A mile(3,1)=sum(ofmil);
A mile(4,1)=sum(inmil);
A-mile(5,1)=sum(fmil);
103
ToMile(l,tlO)=sum(A-mile(:,1));
end
,%
in excel
Writing the results
file
xlswrite('F:\Data\MappingData\Cost.xls',unitcost,'Solution','A2:A2');
'Al:Al');
xlswrite('F:\Data\MappingData\Cost.xls',{'Cost'},'Solution',
xlswrite('F:\Data\MappingData\Strategy.xls',Management,'Solution','A2:E39');
xlswrite('F:\Data\MappingData\Strategy.xls',prodvolu,'Solution','F2:F39');
xlswrite('F:\Data\MappingData\Strategy.xls',{'Dilution'},'Solution','Al:Al');
xlswrite('F:\Data\MappingData\Strategy.xls',{'Onsite
Treatment'},'Solution','Bl:Bl');
xlswrite ('F: \Data\MappingData\Strategy. xls', {'Offsite
Treatment'},'Solution','Cl:Cl');
xlswrite ('F: \Data\MappingData\Strategy. xls', {'Underground
Injection'},'Solution','Dl:Dl');
xlswrite ('F: \Data\MappingData\Strategy.xls', {'Fresh
Water'},'Solution','El:El');
xlswrite ( 'F: \Data\MappingData\Strategy. xls', {'Produce
volume'},'Solution','Fl:Fl');
The various user defined function used in the model are described as follows:
% This function gives cost per volume (in gallon) for trucking
function cost=transportation (dis miles)
% Normalized water shipment quantity in gallon per day
Vol=
10000;
% Truck
LT=2;
loading time,
hr
hr
%
% Truck unloading time,
ULT=2;
% Heavy vehicle average
Avg speed=35;
speed,
mph
RTHD=dis miles;% trip distance in miles
RTHT=RTHD/Avg speed;% trip haul time in hr
Cap=5000;% capacity of truck in gallons
NRT=Vol/Cap;% total number of roundtrips
driver time
DT=(LT+ULT+RTHT)*NRT; % total
mileage=10; % miles per gallon
Fuelconsume= RTHD/mileage; % gallon
% Economic inputs
Fuelcost=3.5;% $/gallon
LaborCost=100;
% $/hr
Totallabor=DT*LaborCost;
TotalFuel=Fuel consume*Fuel cost;
CPM=(LaborCost/Avgspeed)+(Fuelcost/mileage);
cost=CPM*dismiles/42; % cost per gal
end
% cost per gal mile
t This function determines the output water quality
% from a onsite lime softening treatment plant
function [V1,V2,V3]=onsitequality(a,b,c)
%% removal efficieny
104
% residual TDS left
rl=1;
% residual Hardness lest
r2=0.1;
% residual suspended solids
r3=0.1;
% Outflow water quality
Vl=a*rl;
V2=b*r2;
V3=c*r3;
end
% Determines the output water quality for offsite desalination plant
function [V1,V2,V3]=desalquality(a,b,c)
%% removal efficiency
% residual salinity
rl=.2;
% residual hardness
r2=0.10;
% residual suspended solids
r3=0.10;
% Output water quality
Vl=a*rl;
V2=b*r2;
V3=c*r3;
end
% Reads the temporally distibuted salinity profiles from excel
% HF wastewater
function b=wsalinity(d)
% read the total planning days
total operation=xlsread('F:\Data\InputModelData','Sheetl ,'B5:B5');
b=zeros(l,totaloperation+10);
file='F:\Data\HPDIproducedWaterQuality\saltquality';
range='12:040';
% reads the fitted distribution frm excel file
sa=xlsread(file,'Fits',range);
b=sa(:,d);
end
% Reads the temporally distibuted hardness profiles from excel
% HF wastewater
function b=wscales(d)
% total planning days
totaloperation=xlsread('F:\Data\InputModelData','Sheetl','B5:B5');
b=zeros(l,totaloperation+10);
file='F:\Data\HPDIproducedWaterQuality\calquality';
range='12:040';
% reads the fitted distribution frm excel file
sa=xlsread(file,'Fits',range);
b=sa (:, d);
end
% Reads the temporally distibuted turbidity profiles
% HF wastewater
105
from excel
function b=wturbid(d)
% reads total planning days
totaloperation=xlsread('F:\Data\InputModelData',
b=zeros(1,totaloperation+10);
'SheetI',
file='F:\Data\HPDIproducedWaterQuality\ssquality';
range='I2:040';
% reads the fitted distribution frm excel file
sa=xlsread(file,'Fits',range);
b=sa(:,d);
end
106
'B5:B5');