SPE-189730-MS
Practical Reservoir Management Strategy to Optimize Waterflooded Pools
with Minimum Capital Employed
Alireza Qazvini Firouz, Maureen Olisakwe, Blaine Hollinger, Dante Vianzon, and Michael Kenny, Husky Energy Inc.
Copyright 2018, Society of Petroleum Engineers
This paper was prepared for presentation at the SPE Canada Heavy Oil Technical Conference held in Calgary, Alberta, Canada, 13-14 March 2018.
This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents
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Abstract
In this consistently low oil price environment, where infill drilling and new field developments struggle
to meet economic metrics, production optimization continues to be a focus and driver for the industry.
Currently, waterflooding contributes significantly to global oil production and is one of the main nonthermal techniques that can be applied to increase pool recovery. Although proper reservoir management
of assets under Waterflood (WF) is critical to reaching the highest recovery factor (RF) possible, it is
difficult to achieve and maintain given the inherent dynamic nature of the production mechanism. Further,
to achieve optimum reservoir management while providing the opportunity to leverage alternative enhanced
oil recovery (EOR) technology, the existing subsurface and surface infrastructure should be fully optimized.
By optimizing the subsurface and surface infrastructure in parallel with achieving optimum reservoir
management it will result in higher capital efficiency while improving key economic metrics such as
operating expenditure (Opex), reserve replacement ratio, depreciation depletion & amortization (DD&A),
and overall earnings. Given the existing challenges that include reservoir conformance problems, lack
of reservoir energy, excess fluid production, wellbore and pipeline integrity issues, and infrastructure
constraints, how to fully optimize the current infrastructure while achieving optimum reservoir management
in parallel is the main question and challenge.
Husky Energy’s medium oil reservoir management strategy has been highly successful in reinforcing
WF as a sustainable long-term recovery method. This paper will present a practical workflow to tackle
the challenges highlighted by using a systematic reservoir and production engineering approach with
minimum additional capital expenditure (Capex). First, a robust framework was developed to answer three
main questions: "What is happening?", "Why is it happening?" and "How can it be improved?". Then, a
comprehensive dynamic surveillance methodology, consisting of both numerical and analytical techniques
and a 10-step workflow for optimizing a WF project, is discussed. This is followed by the results achieved
by employing this strategy in three of Husky’s WF fields; the Wainwright Sparky, Wildmere Lloydminster
and Marsden-Manitou Sparky pools located in the Lloydminster oil block. The positive impact that this
reservoir management process has had on all key financial metrics will be discussed. As an example, since
the beginning of the optimization initiative the Wainwright and Wildmere pool production has increased
21% and 23% respectively, while the Opex has decreased by 33% and 42%, respectively. Further, since
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implementing a similar strategy in 2016 at the Marsden-Manitou WF, its production has increased by 30%
and its Opex has decreased by more than 18%. Finally, this paper will present a WF protocol check list that
has been developed as a guideline for engineers who need to optimize pool performance even in a capital
constrained environment.
Introduction
Waterflooding contributes significantly to global production and is one of the main non-thermal techniques
that can be applied to increase pool recovery. Although proper reservoir management of assets under
Waterflood (WF) is critical in reaching the highest recovery factor (RF) possible, it is difficult to achieve
and maintain given the inherent dynamic nature of the production mechanism. Further, to achieve optimum
reservoir management while providing the opportunity to leverage alternative enhanced oil recovery (EOR)
technology, the existing subsurface and surface infrastructure should be fully optimized. By optimizing
the subsurface and surface infrastructure in parallel with achieving optimum reservoir management it will
result in higher capital efficiency while improving key economic metrics such as operating cost (Opex),
reserve replacement ratio, depreciation depletion & amortization (DD&A), and overall earnings. Given
the existing challenges that include reservoir conformance problems, lack of reservoir energy, excess fluid
production, wellbore and pipeline integrity issues, and infrastructure constraints, how to fully optimize the
current infrastructure while achieving optimum reservoir management in parallel is the main question and
challenge.
This paper presents a comprehensive, yet practical, strategy for managing Waterflooded pools. The
main objective is to support engineers and geoscientists in optimizing and managing their WF assets in a
systematic way, specifically when they have access to valuable historical pool/field/well data that can be
efficiently utilized.
The first step of the Improve Oil Recovery (IOR) process is to capture the upside potential by careful
interpretation and analysis of historical data while gaining an understanding of the current surface and
sub-surface constraints. As the production life cycle reaches the decline phase it coincides with an aging
infrastructure that typically requires additional maintenance capital to maintain operations. As illustrated
in Figure 1, this creates a challenging environment for any production optimization initiative as available
positive cash flow becomes increasingly lower and lower with time. Therefore, developing a systematic
approach in reviewing the data to find the most economical solution is necessitated.
Figure 1—Production Life Cycle Model and impact of optimization
For any maturing WF oil field, a major challenge is maintaining a positive netback (profit) while the
asset’s total production is in decline. As oil production declines, the operating cost will inversely increase on
a per unit, or per barrel of oil produced, basis. The result is an ever-decreasing netback that will inevitably
reach $0, the shut-in point highlighted in Figure 2. To counter this, optimizing the current infrastructure and
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incorporating proper reservoir management will help lower the overall production decline rate while also
reducing the total operating costs of the field. Collectively this will have a twofold impact on increasing the
netback while extending the overall economic life of the field considerably, as demonstrated in Figure 2.
Figure 2—Impact of production optimization and decline rate reduction on increasing profit and extending well life
Lloydminster Oil Block
The Wainwright, Wildmere and Manitou WFs are located in the Lloydminster Heavy Oil belt in the
provinces of Alberta and Saskatchewan, Canada, highlighted in Figure 3. The geology of the Lloydminster
oil belt is of the early Cretaceous Mannville Group. Wainwright produces from the Sparky C and D sand,
with the majority from the Sparky C. Wildmere produces from the Lloydminster sand and Manitou produces
from the Sparky A, B and C sand.
Figure 3—Lloydminster Oil Block
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Wainwright
The Wainwright area is located NE of the town of Wainwright, Alberta, in Township 045 and 046, Range
06W4M. The Wainwright pool started producing in the 1950’s and waterflooding commenced in the 1960’s
and 70’s. The area is divided into two AMU (asset management unit); Wainwright South (WWSM) and
Wainwright North (WWNM) and although Husky is the operator the WF has been further subdivided into
19 WF units and leases because of differentiating working interests and partners. Each unit is operated
through its own battery and, as illustrated in Figure 4, flowlines connect every well across 14 Sections of
land. Although the majority of the producing wells have been completed with rod pumps, there are a few
that have been completed with rotary pumps. The WF has a line drive pattern with an approximately 35%
pool recovery factor to date.
Figure 4—Wainwright Producers (green color), Injectors (blue color) and flowlines - T45, 46-R6W4
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Figure 5—Wainwright Type Log: Sparky C Ney Pay Isopach (27%, 6 ohms) - 1D0/15-33-045-06W4/0
Wainwright Sparky Pool Geology
The producing formation in the Wainwright area is made up of a major regional portion and a minor lithic
portion in the most northern section. The regional Sparky formation is of the early Cretaceous Mannville
Group, specifically the Sparky C (Wainwright sand) and to a much lesser extent the Sparky D sand. The
Sparky C is a regional sand, coarsening up, which grades from shale to very fine grained sand (reference
Figure 5). It is sourced from the west and distributed south west to north east, creating a permeability trend
in said direction (Baker and Gregor 1989). Typical thickness for the Sparky C sand is 4-5m. Porosity and
permeability increases upward as well, with average porosity of 31% and average permeability of 600 md.
The lithic Sparky C member in the most northern portion of the Wainwright area is different sandstone than
the majority of the Wainwright pool. The regional Sparky C has been interpreted to be eroded and replaced
by tighter lithic sand (sand made from broken rock, not quartz). The lithic components are not found in the
regional Sparky C sandstone. The lithic Sparky C interval is about 5m thick. The cleanest most porous sand
is at the base of the interval and is typically 30-31% porosity. The middle portion of the interval can be
shaly or cemented sand. The upper portion has sand which is between 18-24% porosity. The overall Sparky
C profile is that of a fining up sequence with the vast majority of fluid flow through the lower portion. The
permeability trend is similar to the regional Wainwright, in a NE/SW direction. Baker and Gregor (1989) and
Baker et.al (2012) have evaluated Wainwright field’s formation and fracture characteristics in more detail.
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Figure 6—Wainwright Pool WF Correlation Study
Figure 6 shows historical production and injection rates along with the WOR and GOR data. The injection
profile can be easily correlated with the oil production profile. As noticeable in Figure 6, for the period
2007 till 2013, the oil production decline is steeper than the reservoir natural decline rate due to issues with
the injection system during this time. In Q4 2013, Husky started a WF optimization project in which this
detailed reservoir management framework was developed and implemented. Since beginning this initiative
the steep oil production decline was arrested, oil production rates increased in the first year, and the oil
production rates were then held flat for the two years’ following.
Wildmere
The Wildmere area is located between Lloydminster and the town of Wainwright, Alberta, in Township
047, Range 04W4M. The Wildmere pool started producing in the 1970’s, with waterflooding commencing
in the 1980’s. It is operated as one unit with all wells connected via flowline to a central battery. Similar to
Wainwright, Wildmere consists of various partners and working interests and Husky acts as the operator.
In comparison to Wainwright, Wildmere is setup as injector pairs with surrounding producers that covers
an area of about 6 Sections of land, reference Figure 7. All of the Wildmere producers are completed with
rotary pumps.
Wildmere Lloydminster Pool Geology
The producing formation in the Wildmere area is the Lloydminster formation of the early Cretaceous
Mannville Group. The Lloydminster is a coarsening upward cycle, which ranges from shale to very fine
grained sand (Figure 8). It is sourced from the east and distributed in a NE-SW orientation, which creates
a permeability trend in said direction. Typical thickness for the Lloydminster sand is 7-10m. Porosity
and permeability increases upward as well, with 31% average porosity of and permeability ranging from
400-4000 mD. The Lloydminster sand is trapped by an up-dip shale filled channel (incised valley). Structure
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varies from about 1m above sea level to approximately 11-12m above sea level, with local and varied water/
oil contacts.
Figure 7—Wildmere Producers (green color), Injectors (blue color) and flowlines - T47-R4W4
Figure 8—Wildmere Type Log: Upper Lloyd Net Pay Isopach (27%, 6 ohms) - 1C0/06-19-047-04W4/0
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Figure 9 shows historical production and injection rates along with the WOR and GOR data. There is an
evident steep decline from 2006 until mid-2013 due to un-optimized injection profile of the Wildmere field.
Despite efforts from polymer gel conformance treatment campaigns to plug the high permeability streaks
and channels created by injectors, field performance could not be improved or sustained during this time.
As a result, Husky initiated the WF optimization project in Q4 2013 to optimize the performance of the
WF scheme and field oil production prior to applying any additional polymer gel conformance treatments
or alternative EOR technology.
Figure 9—Wildmere WF Correlation Study
Manitou
The Manitou area is located SE of Lloydminster and NE of Manitou Lake in Township 044, Range 28W3M
and within the Marsden heavy oil field. Manitou started producing in the 1970’s, with waterflooding
commencing in the 1980’s. It is divided into four units; each connected via flowlines to their own respective
battery. Husky is the owner and operator and the Manitou WF wells cover an area of about 8 Sections of
land, reference Figure 10. Although the majority of the producing wells have been completed with rotary
pumps, there are a few completed with Electrical Submersible Pumps (ESP).
Manitou Sparky Pool Geology
The producing formation in the Manitou area is the Sparky formation of the early Cretaceous Mannville
Group. It is composed of stacked sequences of predominately Sparky A, B, C and some discrete Sparky
D and GP sands. These unconsolidated stacked sands can range from a few meters to over 15m in total
thickness of high quality clean sand (Figure 11). The Sparky sands just North of Manitou Lake has been
interpreted as a marine deltaic/shore face environment with channels bringing in sediment from the west
and being deposited in a NW-SE trending shore face.
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Figure 10—Manitou Producers (green color), Injectors (blue color) and flowlines - T47-R4W4
Figure 11—Manitou Type Log: SPRK B Net Pay Isopach (27%, 4 ohms) - 191/06-20-044-26W3/0
Figure 12 shows historical production and injection rates along with the WOR and GOR data of Unit 1, as
an example. Even though the total injection stream had been kept close to constant from 2006 until 2013, the
steep oil production decline could not be mitigated. This is largely due to un-optimized individual injection
rates that resulted in an inefficient pattern sweep with severe conformance issues. WF optimization was first
attempted in 2013 with only partial success. After the successful WF management strategy implemented in
Wainwright and Wildmere, however, a similar process was then incorporated in Manitou, starting with Unit
1 in 2016. Since beginning this initiative the oil decline was arrested, the oil production rates have increased
and key performance indicators (KPI’s) have steadily improved.
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Figure 12—Manitou-Unit 1 WF Correlation Study
Table 1 is a summary of the reservoir and formation properties of these three pools. In addition, for
each WF, injection water is pumped from the respective batteries, through the flowlines, to the injectors.
Producers pump the emulsion through the flowlines back to the individual batteries where the free water
knockout separates the oil and water. Additional make-up water required for reinjection is sourced from
dedicated source water wells, via. direct flowlines, and trucked in produced water from the surrounding
area. In the following sections different examples from these three pools have been used to highlight some
of the main steps in performing WF reservoir management and optimizing each pool performance.
Table 1—Summary of Reservoir Rock and Fluid Properties
Reservoir Properties
Wainwright
Wildmere
Marsden-Manitou
Formation
Sparky C & D
Lloydminster
Sparky
Lithology
Sandstone
Sandstone
Sandstone
Avg. Porosity
31%
31%
30%
Permeability
300-1200 mD
400-4000 mD
200-4000 mD
Reservoir Temp.
26 °C
22 °C
21 °C
Initial Reservoir Pressure
4830 kPa (715 Psia)
4800 kPa (710 Psia)
4600 kPa (682 Psia)
Avg. Initial Sw
26 %
26 %
28 %
Avg. Pay
4m
5m
6m
Oil API
22°
18°
13-19°
Live Oil Viscosity (cP)
20 – 40
90 – 900
100 – 1000
Dead Oil Viscosity (cP)
90-120
~ 500-3000
500-2500
Bubble Point
4830 kPa (715 Psia)
4800 kPa (710 Psia)
4600 kPa (682 Psia)
Rs (Solution GOR)
95 SCF/STB
68 SCF/STB
65 SCF/STB
1.041
1.03
1.032
Bo (Volume Factor) @ Pb
Current Average
Reservoir Pressure (Psia)
230 - 600
230-380
230-525
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Reservoir Properties
Wainwright
Wildmere
Marsden-Manitou
Depth (m)
~ 661m
~ 632m
~ 650m
GOC
37 m above sea level
7 m above sea level
---
WOC
20-24 m above sea
level
4.5 m below sea level
24 m above sea level
Maximizing WF Oil Recovery with Minimum Capital Expenditure
Waterflood RF is the function of aerial sweep efficiency (EA), vertical sweep efficiency (EV) and
displacement efficiency (ED) (Ahmad, 2006 and Baker, 1997). EA*EV is the volumetric sweep (EVOL) of the
reservoir by the injected water. ED is function of primary depletion, Krw & Kro, μo & μw. EV is function of
rock property variation between different flow units and EA is function of mobility ratio, pattern, directional
permeability, pressure distribution, and cumulative injection (Ahmad, 2006 and Cobb, 2005). ED requires
a relatively more intensive capital expenditure (Capex) program to be increased. Oil and water viscosities,
as well as relative permeability will be improved toward a higher ED by utilizing tertiary recovery methods
such as, application of heat to reduce μo and chemical flooding such as, alkaline, surfactant and polymer
(ASP) or polymer flooding to increase μw and improve Kr. However, EA and EV (and EVOL) can be maximized
by essentially optimizing the injection stream with minimal Capex. Therefore, optimizing the injectors and
subsequently producers are the key in optimizing the WF performance. To this end, important objectives
for any WF project include (Cobb, 2005):
1.
2.
3.
4.
5.
Properly located injection wells that support an appropriate WF pattern;
Inject water where the remaining oil saturation is higher;
Closely manage injection profile;
Keep fluid levels in a pumped off condition; and
Balance injection and withdrawals.
Reservoir Management Strategy: A Systematic Asset Management Workflow
Given the dynamic nature of displacement recovery mechanism, production optimization is defined as
managing production of hydrocarbons as things change over time. Therefore an interactive framework
was developed to advance the implementation of a dynamic WF optimization process for three Western
Canadian medium oil fields, described in the previous section. The main objective is to optimize production
and injection rates of already developed pools with the parallel goal of reducing Opex and increasing
capital efficiency. Figure 13 illustrates this 10-step workflow. The framework was developed to answer the
following questions:
1.
2.
3.
4.
What and why is it happening?
How can the performance be improved, or what is the optimum design of the solution?
Where and how to start?
How can it be sustained?
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Figure 13—Practical WF Optimization Workflow (A Systematic Reservoir Management Strategy with Minimum Capital)
Pre-Optimization: A Critical Step in Maximizing Efficiency
As shown in Figure 14, the subsurface and surface are part of an interconnected system. Any changes or
adjustments to optimize the reservoir fluid flow need to be adjusted according to the surface conditions
and constraints. It is for this reason a Pre-optimization review of the current production system model that
includes the reservoir, wellbore, wellhead and the production processing unit, is required. In addition, any
well or pool optimization practice operating in a captial constrained environment necessitate more detail
evaluation that considers all the current field constraints and challenges.
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Figure 14—Understanding Constraints in Production System Model (Pre-Optimization Step)
Reservoir Surveillance: A Parallel Approach of Analytical Techniques with Numerical Evaluations
Geological Review and Mapping Trend:. A geological and petrophysical study is a fundamental
cornerstone of good reservoir engineering analysis and is an essential prerequisite to surveillance analysis.
It is absolutely critical that the engineer develops an understanding of the reservoir geology as they proceed.
In particular the engineer should concentrate on large scale permeability and porosity trends, as well as
continuity. In other word, the engineer should concentrate on hydraulic flow units and correlate static
properties to their dynamic attribute. When examining geological maps, it is very instructive to include
production map such as, bubble plot or contour map display of water cut, current oil/gas/water rate, and
current reservoir pressure to correlate the geological inferences. The engineer and geologist should lay the
horizontal permeability, porosity, net to gross, net pay, reservoir quality maps out beside the production
maps to seek out common trends and visually see whether permeability trends match, for example, cum
oil produced.
Surveillance Techniques. After reviewing geological and petrophysical maps such as top and base
structures, isopack, and saturation, porosity and permeability distributions and correlating static geological
features with fluid flow dynamics, a systematic reservoir and production system analysis has been
undertaken to find out the best strategy in optimizing injection and production rates. Several analytical
techniques such as volumetric calculation, reservoir and production diagnostic tools, well performance
analysis, decline evaluation, communication analysis and tracer studies parallel with numerical method
mainly Streamline simulation have been used to characterize dynamic fluid flow pattern.
The reservoir surveillance started with high level pool performance analysis, including production and
injection rate, ratio and trend analysis on oil, total fluid, water injection, and gas rates, then conformance plot
evaluation on OOIP, RF, HCPV Inj, MOVPV Inj, EUR, and etc., then communication study and pressure
and production maps analysis. Followed by pool level analysis, WF patterns were designed. Each pattern
include group of one injector with the offset producers which are getting support. First, static (fixed) patterns
were created based on wells configurations and locations, and allocation factors were assigned to the shared
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producers based on geometry. Then, fixed patterns were modified and dynamic pattern were designed based
on Streamline simulation, tracer/dye test results, and production-injection communication study. Finally,
allocation factors were adjusted accordingly and the final patterns were used for calculating injection targets.
Classical WF Surveillance vs. Dynamic Approach. Traditionally the following steps are known to be the
essential steps in doing a proper WF surveillance (Baker, 1998):
1.
2.
3.
4.
5.
6.
Geological characteristics
Reservoir rock and fluid property evaluation
Communication analysis of well pairs (injector – producer pairs)
Geometric pattern design and static allocation factors determination
VRR and volumetric calculation
Initial injection target calculation
Followed by the above steps and after determining the initial injection targets based on geometric model
and voidage replacement ratio (VRR), additional steps were pursued as listed below to fine tune the
geometric model:
7. Well pairs communication and connection study
8. Streamline simulation to integrate production/injection rates to the geology and well locations
9. Conformance problems analysis using tracer / dye tests and reservoir diagnostic plots
10. Revise the WF pattern design and configuration
11. Adjust the allocation factors
12. Determine new injection target or revise the previous calculated ones, if needed.
Static patterns are based on injection vs. production ratio (VRR) of each pattern and one can estimate
influx or efflux from pattern based on only VRR. In addition, Well allocation factors are independent of
time and flow rate. However, in dynamic surveillance approach the geometric model is occasionally finetuned with allocation factors, even pattern configurations, being modified throughout time, marked by well
events and subsurface fluid flow changes. Therefore, reservoir connectivity will properly be captured and
interaction of geology, well locations and rates will be quantified. In addition, shut-ins, restarts and new
drills will be properly taken into consideration and an appropriate injection target will be implemented to
maximize the WF sweep.
After finalizing the pattern design and fine tuning the allocation factors, injectivity analysis was
conducted using water injection rates and pressures with calculated Hall plot and slope of Hall (Baker et.al,
2012) in order to identify good vs. ineffective injectors. In addition, productivity and well performance
analysis were completed and WF units (smaller area within the pool that its wells are connected to the
same battery) were ranked based on the number of under produced wells with steady and stable WOR trend
and nearly flat oil rate. Figure 15 and Figure 16 show static and dynamic patterns designed for MarsdenManitou area, shown as an example, and Figure 17 shows the Streamline model built for this area which
was used for dynamic surveillance evaluation. As displayed in Figure 16, the dynamic patterns have more
irregular shapes compared to the static pattern (Figure 15) and patterns’ boundaries are overlapped because
of incorporating the well pair connections, conformance problems and production/injection trends to the
geometry based pattern.
Three types of producer performance were identified; 1st: Normal Performance: wells with relatively
constant liquid (or fluid: oil plus water) rate, smooth and steady WOR increase, and natural decline of oil
rate, 2nd: Damaged wells: wells with both oil and liquid rate sharply decline due to either lack pf pressure
support or near wellbore damage caused by a positive skin, and 3rd: Under-Produced Wells: wells with steady
WOR increase and nearly flat oil rate. The last type has the highest potential and to be considered first for
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any optimization initiative including single well optimization and/or pattern performance improvement by
optimizing the injection rate.
After getting all the above parameters reviewed, injection targets calculated based on; first volumetric
and VRR, then adjusted based on geology and location of wells and perforations w.r.t. each other, followed
by additional tuning based on production-injection historical trends and correlation, as well as conformance
issues. Finally, optimization began with adjustments to the offsetting injection rates of the under-produced
wells that presumably have a higher Sor and unswept patterns.
Figure 15—Static Patterns - designed based on well locations and geometry. Grey
shaded area indicates the shale channels crossing the sparky pool (Manitou field)
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Figure 16—Dynamic Pattern adjusted using Streamline simulation and Tracer tests results. Shale
channels were removed and battery location were added for better clearance (Manitou field)
Figure 17—Streamline Model built for Manitou field
VRR Management. Figure 18 illustrate the iVRR (instantaneous voidage replacement ratio) and cVRR
(cumulative voidage replacement ratio) of different patterns in Wildmere, as an example, along with the
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pool’s overall VRRs (black line) since the start of the WF till the onset of the optimization. As can be
observed, even though the pool VRR has been managed at around 1.0, the patterns’ VRR were not optimized.
However, Figure 19 shows VRR’s since onset of reservoir management that indicates how most of the
patterns’ VRR has been improved by optimizing injection rates and ultimately directing water to the right
place, further mitigating challenges between injectors and producers. Although the aim is to balance pools’
injection and production by maintaining a VRR of 1.0, as dictated by material balance theory, optimum
ratios of 0.6 to 1.4 have been obtained for each pattern depending on the degree of communication and
conformance issue. A couple of reasons that may impede a pattern reaching a VRR of 1.0 include; dealing
with a relatively mature waterflooded pool with many well events that have changed the initial reservoir fluid
condition, and having medium oil which is twenty to two thousands heavier than conventional reservoirs
that caused unfavorable mobility ratio and consequently preferential flow. As a result, some of injectors
might have been slowed down to mitigate the above challenges. However, the target is to maintain an overall
pool’s VRR of 1.0.
Figure 18—Wildmere WF patterns and pool’s iVRR and cVRR: Pre-optimization Condition
Figure 19—Wildmere WF patterns and pool’s iVRR and cVRR: Post-optimization
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WF Injection System Management
After performing WF surveillance and calculating the right injection targets with additional adjustment
based on interactions of geology, rates and conformance issues, the injection system needed to be managed
to implement the targets. Otherwise, the targets need to be modified and the surveillance process to be reiterated if the infrastructure set up could not be adjusted with marginal capex. Generally, there are three main
concerns in this regard, and for any WF project: right volume, pressure constraints and conformance issue.
1. Volume Constraints. The first parameter to look at before implementing the recommended injection
target is injection volume and understanding whether there is enough water supply to meet the targets?
Water sources from dedicated water source wells, recycled produced water and available trucked in water
from offsetting producers in the area should be quantified first. There are two polar challenges that exists
when managing the volume of water needed for a mature WF project; lack of water supply or excess water
to handle. In either scenario, the reservoir engineer should re-adjust the injection targets in the context of
the above constraints. Other relevant concerns that are project specific include compatibility issues between
injected water and in-situ brine, and whether or not filtering of recycled water is required or not.
2. Pressure Constraints (MWHIP or MOP). The next concern in managing water injection systems
is managing the injection pressure and understating the source of pressure constraints. As an example,
injectivity issues are common problems in many injection schemes. The best practice in mitigating any
injection pressure challenge is first to understand the source of the problem. For instance, an injectivity
issue might be due to the lower reservoir permeability or a positive skin caused by scale built up near the
wellbore. The later could be addressed with proper stimulation techniques such as acidizing, foam injection
or solvent treatment. In addition, the MOP (maximum allowable pressure) should also be identified and
compared to the formation fracture pressure.
3. Conformance Control. The third concern in optimizing WF performance is conformance issue which
might have been created by excessive injection rate and resulting channelized flow between injector and
producer, induced fractures, or high permeability streaks in a heterogeneous reservoir, particularly in semi
consolidated or unconsolidated formations. These channelized flow may not be fully treated once created,
unless attempting a conformance treatment technology such as polymer gel injection to plug the higher
permeability streaks. However, the injected water can be directed to the right place to mitigate further
channeling and improve sweep efficiency (EVOL =EA*EV) by optimizing injection rate. To do so, the most
important step is to properly characterize these channels and induced fractures. Couple of approaches has
been taken to properly evaluate the conformance issue in three fields; Reservoir Diagnostics plots along
with qualitative dye testing, and quantitative tracer study. Reservoir diagnostic tools include Chan and Hall
plots along with other related production and injection rates and pressure data (Chan, 1995 and Baker
et. al, 2012). Chan plot is WOR and WOR derivative vs. cumulative injection rate (Figure 20). This plot
can be used to indicate channeling and coring on the producers. In addition, the Hall Plot analysis, first
presented by Hall in 1963, enables engineers to draw conclusions about average injectivity performance. In
a simplified version Hall Coefficient is cumulative wellhead pressure which is plotted against cumulative
injection volume (Figure 21). These diagnostic tools need to be used along with the other evidence to
validate the interpretation. For instance, visual dye testing is useful when combined with this approach.
The main outcome of this analysis is characterization of the current communication between well pairs and
investigating the channels. The second approach is utilizing measurable tracer or dye testing, for which a
tracer is injected in the reservoir through the injector and offset producers will be frequently sampled. Then,
the concentrations will be plotted vs. time and one can interpret the results to characterize the fluid flow
condition. The following section provides more detail information utilizing the above strategies and using
Wildmere and Wainwright data as examples.
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Figure 20—Chan Plot example which indicate channeling – Wildmere Field
Figure 21—Hall Plot for Injectivity Analysis (IHS Harmony, 2017)
Practical Approaches in Evaluating Waterflood Conformance Issue
Reservoir Diagnostic Plots. To integrate well location with historical trends and diagnostic tools, Chan and
Hall plots were plotted for all the patterns in their respective location on a field wide map. Then any evidence
was recorded along with dye and tracer test result. The information then used for flow characterization,
conformance issue evaluation and ultimately tuning injection targets. Figure 22 illustrate one example of
this type of evaluation for only one pattern and show the location of C/03-30 pattern in Wildmere pool
selected as an example for conformance issue treatment.
As illustrated in Figure 23 a channel was created by C/03-30 injector after injecting around 6 MMbbl of
water. This can be observed by a sharp decrease in slope of Hall plot right after injection rate was increased
to over 1,600 bbl/d followed by sharp drop in well head injection pressure. In order to understand with
20
SPE-189730-MS
which producer(s) channel was created this plot was correlated with Chan plots of the offset producers, as
show in Figure 24.
Figure 22—Visual Dye Test Showed Quick Breakthrough (Less Than 3 hours)
Figure 23—Hall Plot along with Well Head Injection Pressure and Injection Rate:
Injectivity Analysis for Conformance Issue Treatment – Wildmere Pool Example
SPE-189730-MS
21
Figure 24—Injector Hall Plot Correlated with Producers Chan Plot to Investigate the Direction of Channelized Flow
Tracer Study. Another approach is using tracer data to characterize the channels and induced fractures
created by injectors (Baker and Gregor, 1989). If properly managed, quantitative tracer study is a powerful
tool in characterizing fluid flow condition and screening reservoir which provides valuable information with
a relatively lower cost. For WF optimization and management it will help asset team to identify of interwell communication, confirm the presence of channeling, diagnose poor vs. strong sweep efficiency and
identify its direction, identify potential injectors suitable for conformance treatments, and then optimize
injection rate and consequently optimize WF by rectifying flow anomalies prior to EOR application. In
addition it can help extending the life of pool and making the EOR screening process more efficient in a
cost effective way compared to logging, coring, pressure testing and the other screening tools. Further, it
can help indicating of layering and permeability variation, heterogeneity, presence and extension of high
permeable thief channel, and fluid flow characteristics: Linear vs. non-linear flow. Therefore, it will reduce
risk of EOR project investment and well treatment.
Examining Tracer Response Curves. Tracer responses curve are the plot of tracer concentration of each
sample vs. time or vs. no. of samples. Three parameters are essential in examining this curve; Time of
breakthrough of tracer, the peak concentration of tracer, and the shape of the tracer production curve.
Time of breakthrough of tracer depends on the distance between the injector and producer, the effective
permeability of the formation, and the total pressure drop between the injector and producer. The peak
concentration of tracer is a function of the distance from injector to producer, transverse cross sectional area
of transporting zone, amount of tracer injected initially, and amount of mixing between water containing
tracer and formation water. The shape of the tracer production curve is dependent on whether flow is nonlinear or linear and the heterogeneity of the reservoir. (Baker and Gregor, 1989, and Baket et al., 2012)
Husky performed a comprehensive tracer study in Wainwright, Wildmere and Manitou using different
types of tracers and dyes. As an example, Figure 25 shows the location of an injector that was tested with
the offset producers which were sample. High concentrations of the tracer were detected at three producers
as interpreted in Figure 26 to Figure 28 (D/08-32, D/02-32 and B/07-32). This confirms that there is fluid
22
SPE-189730-MS
movement between these wells and the injector (INJ D/8-32). The flow of tracer indicates the presence
of highly permeable thief channels from the injector towards the producers. This is confirmed by early
detection of tracer relative to what would be expected theoretically and the lack of detection of tracer at
the other wells closer to the injector. Thief channels are also confirmed by the lack of expected relationship
between peak concentration of tracer and distance between the injector and producer. The flow towards the
wells that demonstrated tracer breakthrough is linear in nature. This is confirmed by fast decline in tracer
levels after the maximum concentration of tracer was observed (Figure 26). The sweep efficiency is poor
in the direction from the injector toward these three producers due to the presence of these thief channels.
In addition it can be concluded that the reservoir is horizontally heterogeneous in the area that was studied
(Figure 27). The other seven producing wells in the test showed no signs of tracer breakthrough despite the
fact that some of the wells are much closer to the injector than D/02-32 and B/07-32.
Figure 25—Wainwright Tracer Study to Characterize Fluid Flow
Conditions-Location of D/8-32 Injectors with its Offset Producers
SPE-189730-MS
23
Figure 26—Quantitative Tracer Result – Wainwright Well: Flow Characteristic Indications
Figure 27—Quantitative Tracer Result – Wainwright Well: Reservoir Heterogeneity Indication
24
SPE-189730-MS
Figure 28—Quantitative Tracer Result – Wainwright Well: Indication of Early Breakthrough
Effective Operating cost and Capital management
For this process to be lucrative it was important that the $/boe of recovering potential incremental oil from
a mature field was critically analyzed; weighing all risks and potential benefits of each approach. To cut
back on capital requirements for optimization, major factors that impacted well performance was reviewed
to ensure that additional spending was justified by all means, especially with the low economic situation.
2014 - 2015, operating expense (Opex) for this region was predominantly ~$30.00. Figure 29 shows a
historical plot of percentage change of $/boe compared to the 2001 base line. The Opex has been increasing
till 2013 and since then with the positive impact of WF reservoir management it has been dropping. Further
analysis of the variable portion of the Opex showed that 45% of this cost was related to well optimization
projects.
Figure 29—Historical operating cost trend
SPE-189730-MS
25
Figure 30—Historical change in production volume
Justifications to optimization project in the Medium oil field required sensitivity study of various reservoir
properties and production operation practices to understand what practices could have the most impact if
optimized. Optimization options the team considered were, adding perforations to reduce injection pressure
on the injectors and possibly access more reservoir on the producers; wellbore cleanout operations on the
injectors; drilling new injectors vs converting existing producers; upgrading the surface lifting equipment
and update to existing flowline gathering network. For ease of data presentation, sample points denote
individual wells. Study was completed on the Wildmere and Wainwright fields.
Converting producers to Injectors vs new injectors
As elaborated previously, the injector/producer pattern is a major factor in determining the reservoir sweep
efficiency in a WF scheme. The WF patterns in Wainwright area originally was designed with an inverted
nine-spot pattern. This has evolved over the years of WF experience in the area to accommodate for reservoir
changes. This section focused on reviewing the variation of pressure response amongst the wells that were
originally oil producers and later converted to injectors. A plot of cumulative oil production versus injection
pressure is presented in the chart below. It is believed that with increased cumulative oil production comes
more voidage and as such, the injection pressure should be lower where there exists lower production.
Based on the results as shown in Figure 31and Figure 33, no correlation could be draw to imply that
injectors that were not former producers performed any better or worse than the counterpart. As a result, it
was decided to continue with the methodology of converting strategic producers to injectors. A good practice
to identify candidates for conversion was to perform periodic dye-testing between injectors and producers
within a water flood pattern. Figure 32 and Figure 34 shows wells with longer production history having
lower injection pressures even at similar injection rates when compared to their counterparts. Dye was
injected into the injectors and monitoring returns from supported producers. Fluorescence dye was used for
this process and the concentration of dye to be used depended on the injection rates. Dye testing confirmed
if the existing injection patterns were still supporting targeted producers and also it helped identify the
producers that had established channels directly to the injectors.
26
SPE-189730-MS
Figure 31—Injection pressure comparison to production history of the Wainwright - Sparky formation
Figure 32—Injection Pressure Analysis of the Wainwright - Sparky Formation
SPE-189730-MS
27
Figure 33—Injection pressure comparison to production history of the Wildmere - Lloydminster formation
Figure 34—Injection Pressure Analysis of the Wildmere - Lloydminster Formation
Impact of Perforation interval vs Injection Pressure
A review of injection pressure vs injection rates across both field showed different results. The Wildmere
field had a close correlation between high injection pressure and high injection rate as expected. However,
the Wainwright Sparky showed some anomaly and had to be investigated further. The perforation density
within the injected formation across both pools was fairly consistent at approximately 13 shots per meter
(SPM). However, it was observed that wells with perforations at the top of the sand body, showed higher
28
SPE-189730-MS
injection pressures in the Sparky formation. However, it was noted that perforated depths may have effect
on the reservoir connectivity, hence higher well head injecting pressure. Based on the outcome of these
trials no further perforations were recommended to improve recovery.
Well Bore Cleanout Practices
A regional challenge in the area was maximum allowable operating pressure (MAOP) of the injectors
approved by the regulatory body. With a concern for scale build up in the injectors, which could impact
well head injection pressures, regular sampling and lab analysis of produced/injected water mostly showed
little or no amount of scale, therefore, scale inhibitor programs were introduced to the water stream at the
facilities. Most tests showed that there existed a higher percentage of suspended oil droplets than anything
in the water samples.
Facility Upgrade
The facility design was already set up such that with the water separation system was by heat breaking up
the emulsion, hence, was no need for extended settling time. The retention time between the treater and
injected water was 24hr<. This was almost a direct injection system in a continuous loop. It can be argued
that higher water temperatures may have helped reduce the oil viscosity and improve sweep efficiency.
Figure 35—Emulsion Treatment Facility Set up
Using Progressive Cavity Pumps (PCP) vs Reciprocating Pump
Fluid level management was great part of the process. The down side of this was its labor intensiveness.
However, it was demonstrated that maintain the fluid level between 3-5 joints of fluid was vital in
maintaining oil influx to the well bore eliminating water bypass due to high watercut and improving sweep
efficiency.
A general perception was to upgrade most of the producing wells to a rotary pump system so as to keep
up with the fluid level management. However, due to limited capital available for this project, the wells
were reviewed on a case by case basis. It was determined that some wells could actively keep up with fluid
level by speeding up the reciprocating pumps. In cases, were the reciprocating pumps were maxed out at
operating speed.
To reduce capital requirements when some of the wells were converted from reciprocating to rotary
systems, it was decided to retain the stick rods instead of changing out the rod string completely to a
continuous rod which is an industry preferred approach to operating rotary pumps. This decision was
sustained because 95% of the wells were vertical wells absence of deviations meant that the box of the rod
SPE-189730-MS
29
connections had limited contact with the tubing walls, hence no concerns of tubing wall loss. Also, in the
event of rod break only affected sections where replaced using a cheaper Flushby truck option instead of
a service rig.
Results and Discussion
The following plots clearly compared the pre- with post-optimization condition. For all three fields oil
production steep decline has been turned around to an incline and then were sustained till today. Various
financial indicators have been employed to review the success of the Waterflood program; however, for
simplicity the incremental oil recovery vs operating cost summarizes the impact additional oil recovery has
affected the bottom-line for Medium oil assets. Wainwright and Wildmere oil production increased by 21%
and 23% with Opex decreased by 33% and 42%, respectively since the onset of optimization initiative.
Upon implementing a similar strategy Manitou oil production increased by 30% with Opex decreased by
more than 18 % since 2016. For Wainwright and Wildmere Opex dropped by $10.0/boe each and total PDP
reserve of 6,900 MSTB net and 10,350 MSTB gross has been added as a pure result of these reservoir
management, all with optimizing the performance of current well and no drilling or spending extensive
Capex. WF was successfully managed; GOR was substantially dropped, indicating the reservoir pressure
was maintained, and WOR steep incline was slowed down or even decreased or flattened.
Figure 36—Wainwright Field Pre. Vs. Post Optimization Performance
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SPE-189730-MS
Figure 37—Wildmere Field Pre. Vs. Post Optimization Performance
Figure 38—Manitou Unit 1 Pre. Vs. Post Optimization Performance
Conclusion
Husky developed a robust reservoir management framework and improved the performance of fairly mature
waterflooded fields in Lloydminster block. The strategy was implemented on three different fields. Despite
SPE-189730-MS
31
all the subsurface and surface related challenges as described in the paper, it has been approved that with
proper strategy and appropriate utilization of historical data the oil production can be increased even in
capitally constraints environment. In addition it has been determined that applying an extensive EOR
technology may not provide the expected result without optimizing the base - the surface and subsurface
set up.
Further, despite the possibility of an adverse mobility ratio, the Wainwright, Wildmere and Manitou fields
responded to an optimized Waterflood approach. Given its dynamic nature of flood and attempt to reducing
the cost of evaluation and capital expenditure, various methods need to be applied in parallel to tweak
patterns performance and improve sweep efficiency, therefore increasing oil production. A combination
multi-disciplinary approach, strong reservoir and production engineering assessment and optimization
strategy and right and repeatable process, along with day to day monitoring are responsible for a recent
change in the higher than anticipated recovery factor till date.
Acknowledgments
The authors would like to acknowledge Husky Energy for consent to present the result and achievements
of this work. In addition, special thanks to filed operation team for all the support during this project.
Nomenclature
AMU
ASP
Bo
Capex
DD&A
EA
EV
ED
EVOL
EUR
HCPV Inj
K
Kr
MOVPV Inj
MWHIP
MOP
OOIP
Opex
Np
Pb
Pi
Qo
QW
QL
Soi
Sor
RF
SW
Vp
= Asset management unit
= Alkaline, Surfactant and Polymer flooding
= Oil Formation volume factor
= Capital expenditure
= Depreciation depletion & amortization
= Aerial sweep efficiency
= Vertical sweep efficiency
= Displacement efficiency
= Volumetric sweep efficiency
= Expected ultimate recovery
= Hydrocarbon pore volume injected
= Absolute permeability
= Relative Permeability
= Moveable pore volume injected
= Maximum well head injection pressure
= Maximum operating pressure
= Original reservoir oil in place
= Operating cost
= Cumulative produced oil (also denoted Qo)
= Bubble point pressure
= Initial reservoir pressure
= Oil rate
= Water rate
= Liquid rate
= Initial oil saturation
= Residual oil saturation
= Recovery Factor
= Water saturation
= Pore volume
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SPE-189730-MS
Winj
WF
VRR
μw
μo
= Water injection rate
= Waterflood
= Voidage Replacement Ratio
= Water Viscosity
= Oil viscosity
SI Metric Conversion Factors
acre × 4.046 873
E+03 = m2
bbl × 1.589 873
E−01 = m3
cp × 1.0*
E−03 = Pa·s
ft × 3.048*
E−01 = m
in. × 2.54*
E+00 = cm
lbm × 4.535 924
E−01 = kg
psi × 6.894 757
E+00 = kPa
*Conversion is exact.
References
Ahmad, T. 2006. Principles of Waterflooding. In Reservoir Engineering Handbook, 3rd edition, Chapter14, 857-1025.
Huston, Texas, Gulf Professional Publishing.
Cobb, W.M., 2005. Reservoir Management Under Water Injection A Worldwide Perspective. 2nd National Meeting on
Secondary and Assisted Oil Recovery, September 8–9, Malargue, Argentina.
Baker, R. and Gregor, V. 1989. Fracture Characterization of the Wainwright Pool, 39th Annual Technical Meeting of the
Petroleum Society of CIM, Calgary, Alberta, JUNE 12 – 16. PAPER NO. 88-39-45.
Baker, R., Stephenson, T., Lok, C., Radovic, P., Jobling, R. and McBurney, C. 2012. Analysis of Flow and the Presence of
Fractures and Hot Streaks in Waterflood Field Cases. SPE Canadian Unconventional Resources Conference, Calgary,
Alberta, 30 October – 1 November. SPE 161177.
Baker, R. 1997. Reservoir Management for Waterfloods, JCPT, Vol. 36 (Issue 04): 20-24, PETSOC-97-04-DAS, DOI:
https://doi.org/10.2118/97-04-DAS
Baker, R. 1998. Reservoir Management for Waterfloods-Part II, JCPT, Vol. 37 (Issue 01): 12-17, PETSOC-98-01-DA,
DOI: https://doi.org/10.2118/98-01-DA
Chan, k.S. 1995. Water Control Diagnostic Plots. SPE Annual Technical Conference & Exhibition, Dallas, U.S.A., 22-25
October, SPE 30775
IHS Harmony, 2017. Surveillance Analysis Theory. November 30, 2017, http://www.ihsenergy.ca/support/
documentation_ca/Harmony/content/html_files/reference_material/analysis_method_theory/surveillance_theory.htm
(accessed Dec. 1st, 2017)
Seright, R.S., Lane, R.H. and Sydansk, R.D. 2003. A Strategy for Attacking Excess Water Production. SPE Production
& Facilities, Vol. 18 (Issue 03): 158-169, SPE-84966-PA, DOI: https://doi.org/10.2118/84966-PA
SPE-189730-MS
33
Appendix
Reservoir Management Strategy Checklist
Part 1 of following checklist will answer the questions like "What and why is it happening"? or essentially
"What is the problem"? After going through the suggested steps in this part, the associated engineer
will have enough understanding of different aspects of the asset such as geology, reservoir mechanism,
production system model, mineral land and lease condition, operation and facility set up, and joint ventures
(if applicable). Given enough high level understanding obtained from part 1, the engineer can start a
thorough evaluation listed in the next part. Part 2 lists the main steps of a systematic analysis in Waterflood
optimization from pool level to pattern/pad level and finally to single well level analysis. The main questions
to be answered here is "How can the performance be improved"? And "Where to start"? (When there are
many wells/units in a larger pool). At end of this part the associated engineer will be able to come up with
the proper recommendation for the field staff to implement for optimizing injection and production rates.
Finally, Part 3 and 4 list the important steps to sustain the growth and to further improve the performance
of the area.
Ultimate goal is:
Optimize Production & Injection Rates
↓ Op Cost & ↑ Capital Efficiency for Any Future Investment
Part 1: High Level Asset Review, Data Gathering & Understanding the Main
Constraints
This is for any new or experienced engineer who takes over an asset and would like to optimize the
performance of the pool. It is the AMU (pool) understanding phase and to answer questions like: What is
happening? Why is it happening? And, then How to develop the Road Map?
1) G&G & Petrophysical Review
A. Review formation characteristics with geologist and geophysicist :
1.
2.
Which formation?
Average properties of the pool?
a.
3.
4.
5.
6.
7.
8.
Pay (h), Porosity (Phi-φ), Swi, Permeability (k), etc.,
Existence of flow barriers and shale bodies around and across the pool
Bottom and/or edge water?
Gas cap across the pool?
Seismic data available for review
Type Log and cross sections
GOC (gas oil contact), WOC (water oil contact) and Shale contacts
a.
Locate Gas Cap across the pool
b.
Locate bottom/edge water across the pool
B. Review geology maps:
1.
2.
Top & base structure maps
Thickness & Isopach maps:
a.
Gross pay, Net pay and NTG pay maps
34
SPE-189730-MS
3.
Porosity, Swi and Permeability values (map) for each well/subsection
4.
Phi*h*So map (to rank different part of pool for future development)
C. Establish local area fracture gradient
D. Kick off geological modeling early, if necessary!
2) Reservoir Characteristics & Description Review
A. Important to understand what controls or limits oil rates first before forecasting. Limiting factors
include:
1.
Well lift capacity
2.
Water handling capacity
3.
Relative permeability
4.
Pressure drop between FBHP (flowing bottom hole pressure) in injector and producer
5.
Pressure drawdown limitation
6.
Volumetric sweep
B. Review well spacing, well configurations
1.
Whether developed/developing on vertical or HZ; select stratigraphic well locations to provide
core for perm/porosity
C. 1
Understand Reservoir mechanism: 1
Solution gas, water influx, etc.
1
Determine initial and current Reservoir Pressure
1
Determine which factor control Recovery:
a.
Heterogeneity, Gravity, Viscosity, Low Permeability, Continuity, etc.
Primary recover vs. secondary recovery
D. Calculate OOIP, GOIP & Pool RF with geologist
E. Obtain current pool oil/water/gas rates, WOR, GOR, Cum Qo to date, and no. of wells.
F. Perform pool summary decline analysis and/or review last year reserve binder:
1.
Determine EUR & Remaining Reserve
2.
Pool decline rate, WOR, OC cut off
3.
Decline method : Exponential, Hyperbolic, Harmonic
G. Determine mobility and mobility ratio (Kw* μoil / Ko* μw)
H. Conduct basic fluid analyses (oil, water, gas) across the pool.
I. Review PVT and reservoir fluid analysis report
J. Review RCA (Routine core analysis) reports and determine end points.
K. Review SCAL (capillary pressure, relative permeability) reports
L. Review feasibility corefloods, mobile So, Sorw
3) Production System Review
A. Current production and Opex vs. budgets vs. forecast or recast
B. Lease Stat Report
C. Wellbore conditions
SPE-189730-MS
35
1.
Artificial lift system: Pump (Pump jack or PCP?) or Gas lift
2.
Wellbore equipment and capacity
3.
Completion system and perforation (casing, frac., jet, etc.)
D. Any production and injection well testing (e.g. fall off test, step rate test and etc.)
E. Sand Production?
F. Water supply: current volume vs. limits
1.
Produced water
2.
Water source wells
3.
Truck in volume
4.
Source well requirements? Any compatibility test?
G. Identify potential source wells and anticipated deliverability
H. Determine maximum fluid production and injection capacity
I. Review regulatory compliance (MWHIP, Allowable production, ect.)
4) Operation and Facility Understating
A. Single-well battery or central processing facility (CPF)?
B. Infrastructure and facility condition
1.
Pipeline integrity
C. Well head & facility constrains
1.
2.
Water injection constraints (pumps, volume, pressure, etc.)
Produced fluid handling: max liquid can treat?
5) Land and lease condition, and JV condition
A. Review mineral land ownership with Landman? Surface leases.
B. Review land expiry.
C. Review Royalties system
D. Review partners and joint ventures
E. Determine working interest, Operating vs., not operating wells/area
6) Analog Reviews
A. Close proximity; similar producing formation
B. Similar Petrophysical and reservoir properties (k, φ, Swc, μoil & ρoil)
C. Similar reservoir mechanism and drive
D. Type curve analyses, statistical comparison
7) For new WF development
A. Expected/anticipate number of wells, infills?
B. Establish desired pattern – Identify injector/producer conversions
1.
2.
With desired well count, what’s total produced fluid expectations and potential injection
requirement
For water injectors, may want bigger perforations to maximize injectivity, especially if TSS of
produced water is high (this completion may be necessary for future polymer injection in vertical
wells)
36
SPE-189730-MS
3.
4.
5.
Big cumulative producing wells may be good injectors and oil saturation will be lower around
these wells
Conduct fluid-fluid compatibility testing, if planning to use a source well
Design wellhead and surface facilities with prospective EOR in mind (ASP, Polymer, Gas and
Solvent, Thermal etc.)
Part 2: Evaluation & Pool Performance Analysis
This part is the evaluation and analysis required to answer questions like: "How can it be improved?",
"Where & When?", and "How to achieve Pool and Well Optimization in a short time even with many
data?" Part 3 is divided to three main steps; first, a high level analysis will be performed to evaluate the
pool, AMU and the reservoir. Then, Waterflood patterns (each one comprises of one single injector-vertical
or horizontal-with its offset producers-vertical or horizontal-that are connected to it) will be created and
analyzed. And finally, single well level evaluation will be performed with appropriate recommendations
to optimize injection and production rate at the end. Need to emphasize that some of the steps may seems
repeated for different levels, however, they are essential to confirm any trend or evidence needed for the
evaluation (Baker, 1997 and 1998)
1) High-level Pool Analysis
A. Pool rates, ratios & trend analyses (Composite Plots)
1.
Plot of QO, QL (or QW) and WINJ, WOR, GOR, and no. of active wells vs. date and vs. cumulative
oil.
a.
b.
To evaluate pool performance in high level!
To determine any stable operating condition!
1.
Plot of QO, QL and WINJ, and pool VRR (instantaneous/cumulative) vs. date.
a.
2.
Determine whether over injecting or under injecting, or normal decline?
Trends in GOR, WOR vs. Date and vs. cumulative Oil
a.
b.
Any recognizable patterns in GOR, WOR relative to where/how injection/production is occurring?
Log WOR vs. cum oil – an indicator of channeling and heterogeneity
1.
Breakthrough time
2.
Wells with early water breakthrough should be identified quickly and correlated with geology.
3.
Determine cum oil produced before breakthrough occurred
c.
Increasing GOR indicates a bypassing problems when cumulative VRR<=1
3.
Plots of cumulative trends in QO, QL (or QW), and WINJ vs. Date
B. Generate Production bubble or grid maps:
1.
Cumulative volume bubble plots (oil/water/gas, GOR, WOR, WC & WINJ)
C. Residual oil saturation mapping (Cumulative production relative to OOIP)
D. Communication analyses:
1.
Plot of liquid, Oil and Winj rates vs. Date
a.
To distinguish Poor vs. moderate or strong communication!
b.
Evaluate reservoir quality, indication of higher perm vs lower perm or tight formation.
E. Conformance Plot Analyses
SPE-189730-MS
37
1.
2.
3.
Plot of RF vs. Date
RF vs. HCPVINJ (HC Pore volume Injected): EUR at each PVI
RF vs. Movable HCPVINJ (Net Injection (Cum WINJ-Cum QW)/Movable HCPV)): Estimate
ultimate displacement efficiency at each PVI
F. Determine EUR, recoverable OIP, remaining oil, and reserve life index (RLI)
1.
Production Decline Analyses or
2.
Correlations
G. Pressure data screening
1.
Coordinate regular-interval fluid level shots – use this information to confirm where additional
injection may be required
2.
Map Static fluid level data (Shut-in BHP – Ave PRE) for Shut-in wells and Dynamic fluid level
(Flowing BHP) for producing wells
H. Field Performance Maps
1.
Plot 1: Plot QO, QL, WOR, and GOR for each well printed in their respective location (Can use
well charter tool or excel):
a.
b.
2.
QO, QL, WOR and GOR, JOF (or Pwf-BHP) and RPM/SPM vs. date for producers
WINJ, WHIP, and BHIP (if available) for injectors
Plot 2: Reservoir Diagnostic Plots for each well in their respective location
a.
Hall plot for injectors and Chan plot for producers
2) Pattern or Pad (for HZs) Level Analysis
A. Pattern/pad design and selection (each pattern is a group of one injector with its offset producers
connected or being supported by the same injector)
1.
Orientation of Waterflood patterns: determine if there is a preferential permeability direction,
natural fracturing, or a combination of in-situ stresses and rock properties that would cause the
formation to fracture in a particular direction during stimulation or injection above parting pressure.
B. Determine production allocation factor:
1.
2.
Static: geometry or well location based allocation factor.
Dynamic: production flow-based allocation factor (Streamline Simulation)
a.
Use any recent dye or tracer test result to tweak the allocation factors.
C. Perform volumetric calculation
1.
2.
Calculate and plot VRRINS and VRRCUM for each pattern
Use VRR with pressure data
a.
VRR<1, P<Pi : Aquifer influx
b.
VRR<1, P<Pi : Over pressuring, out of zone
c.
VRR<1, P<Pi : Lack of injection support
d.
VRR<1, P<Pi : Out of zone injection
3.
Determine area of over injecting vs. under injecting
4.
Calculate 1st injection target set (VRR based target) for each injector.
D. Waterflood correlation Study: plot QO, QL and WINJ, WOR, GOR, and No. of prod. vs. date for each
pattern/pad (rates adjusted with allocation factors)
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SPE-189730-MS
E. RF on pattern/pad basis (optional – really useful if you have OOIP of each pattern)
F. Cross section map for each pattern
1.
Useful to tune up injection targets and capture optimization potential.
3) Water injection analysis
A. Injectivity analyses and well-by-well geological/completion review
1.
2.
3.
Plot of injection rate and WHIP vs. Date
Hall and Slop of Hall plots
Injectivity analyses from Darcy Radial Flow (Seright et. al, 2003):
a.
{injectivity from q/ΔP vs. (Σkh)/141.2·μ·ln(re/rw)}
4.
Any commingled and/or out of zone injection?
5.
Are perf’d in the correct part of the pay? Can we add perfs? Close off perfs?
6.
Are the injection wells situated correctly (i.e. not in the structural high)
B. Cumulative injection plots
1.
2.
3.
Do you have high/low injection areas and how does this tie into production?
Create a grid map to zone cumulative production divided by cumulative water injection!
Review injection build-up tests, step-rate tests (breakdown/frac. pressure) pressure fall-off (k, skin,
capacity/storage, fluid mobility).
C. Conformance evaluation
1.
2.
3.
4.
Signs of rapid breakthrough events, sharply rising fluid levels?
Dye/tracer testing completed recently? (fluorescein, isotopes)
Can mitigate through injector rate reductions?
Attempt variable-rate VR-WF pilot to stimulate production?
a.
If not, investigate gels for permanent fix
5.
Poor volumetric sweep, thief zones
D. Rank injectors (good vs. bad):
1.
Good injectors: better located, lower top/base structure, thicker pay, and higher porosity.
2.
Bad injectors: bad location, higher structure, thinner pay, and low porosity.
E. Facility Analysis (injector)
1.
Summarize each injector constraints:
a.
b.
2.
3.
4.
MWHIP and regulatory compliance
Any injection rate/pressure constraints
What are the injection limits for rate and pressure?
Well constraints? Can you move water around or is it a struggle to just get water away?
Create upset facility go-to injection well list (List of injector which can take more water in case
there is high excess water to handle without negatively impacting the oil production).
4)Production Well Analysis
A. Well-by-well productivity and performance analysis, and geological/completions review: 1
Well specification: Plot QO, QL, WOR, GOR, JOF, Casing Pressure (or FBHP), RPM/SPM, Pump
change date and Pump efficiency vs. date
SPE-189730-MS
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5
Chan plot: WOR and WOR’ vs Cum oil rate, Cum WOR and Cum WOR’ vs. Cum oil rate
2
Is the well pumped off? Whether fluid levels being managed properly?
3
Is sand production being measured? If not, attempts to quantify this should be made on existing wells
and especially in the early times of newly drilled wells (extrapolation)
4
Any commingled zones?
5
Is reservoir perf’d in the correct part of the pay? Can we add perfs? Close off perfs to change inflow
profile? Or etc.
6
Where do the production wells sit in regards to the pay/structure maps? (i.e. not in the structural high)
B. Confirm with geology the locations, perfs etc. based on structure and net pay
C. Recognize the Under produced wells:
1.
2.
3.
Normal well: Constant/flat QL, natural decline of QO, smooth (gradual or steady increase of WOR
or flat WOR’: oil declining naturally!
Damaged well: QO & QL both declining: indicating wither lack of pressure support (GOR is
increasing) or formation damage (Permeability decrease, low reservoir quality).
Under-Produced well: flat QO & QL & WOR: May possess higher upside potential for optimization
a.
The more under-produced wells, the higher upside the area has
D. Horizontal well diagnostics: production logging, spinner surveys, cuttings analyses along HZ length.
E. Flow Capacity Analyses
1.
Kh vs. Depth, Kmax vs. φ
2.
Dykstra-Parsons Coefficient – Permeability Variation (Areal basis)
3.
Lorenz (Flow-Storage) Coefficient Kh vs. hφ
4.
Gravity-Viscous Number (Determine extent of water under-ride)
5.
Mobility Ratio Calculation (typically end-pt value)
6.
Unfavorable relative permeability (especially in late WF stages)
F. Facility analysis (producer)
1.
2.
3.
Well lift capacity or artificial lift constraints (equipment review?)
Flowing Bottom-hole pressure gradient between injector/producer insufficient
Allocation/proration issues, metering for fluid and gas
5)Finalize Injection Targets
A. Pattern/Pad VRR review
B. Tune/Reiterate targets based on:
1.
2.
3.
Volumetric analysis (VRR, material balance)
Geology
Production/Injection history and fluid flow characterization
a.
4.
Evidence of channeling injector-producer communication
Water conformance analysis
40
SPE-189730-MS
6)Where Should Optimization Be Initiated/Focused first (Asset Allocation)
A. Determine under-performing area based on
1.
2.
3.
No. of under produced wells
VRR & unswept patterns
Higher Remaining Recoverable Reserve and OOIP
Part 3: Recommendation, Execution & Monitoring
1)Action item list
A. Implement the injection target and start monitoring any response
2)Periodic well review and Waterflood Management meeting
A. Well speedups, pump changes, equipment optimization
B. Injection rate and target review
C. Optimal joints-to-fluid and corresponding injection targets
D. Review Opex and any initiative to reduce it
E. Revise and update injection targets- as needed
Part 4: Follow up Process and Further Development, if necessary
1)Capital Projects/New Development/Closing
A. Possibility for infill drills?
B. Pattern Modification: New conversions/reactivations?
C. Stimulation (acid, solvent or frac., sand cleanouts)
D. Additional facility investment
E. Any other recommendations or initiatives to enhance production.
2)Kick off EOR Evaluation/Reservoir Modeling/Pilot-Field Implementation
A. Analog Evaluation and Screening
B. EOR lab pre-screening (fluid analyses, SCAL, feasibility studies)
C. Initiate reservoir modelling
D. Re-logging/Coring, if needed
E. Tracer/Dye testing
F. EOR lab and confirmation studies
G. Forecasting and Simulation sensitivity
H. Pilot/Field Design/Implementation