See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/353646141
Assessment and Optimization of Waterflooding Performance in a Hydrocarbon
Reservoir
Conference Paper · August 2021
DOI: 10.2118/207114-MS
CITATION
READS
1
705
3 authors:
Miracle Imwonsa Osatemple
Adekunle Adeniyi
Clean Technology Hub
Afe Babalola University
3 PUBLICATIONS 3 CITATIONS
20 PUBLICATIONS 21 CITATIONS
SEE PROFILE
Abdulwahab GIWA
Afe Babalola University
139 PUBLICATIONS 1,205 CITATIONS
SEE PROFILE
All content following this page was uploaded by Adekunle Adeniyi on 25 September 2021.
The user has requested enhancement of the downloaded file.
SEE PROFILE
SPE-207114-MS
Miracle Imwonsa Osatemple, Adekunle Tirimisiyu Adeniyi, and Abdulwaha Giwa, Afe Babalola University,
Ado‑Ekiti, Ekiti State, Nigeria
Copyright 2021, Society of Petroleum Engineers
This paper was prepared for presentation at the Nigeria Annual International Conference and Exhibition held in Lagos, Nigeria, 2 - 4 August 2021.
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
of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect
any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written
consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may
not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
Abstract
In order to properly meet up with the ever-increasing demand for petroleum products worldwide, it
has become increasingly necessary to produce oil and gas fields more economically and efficiently.
Waterflooding is currently the most widely used secondary recovery method to improve oil recovery after
primary depletion. A crucial component required to conduct an efficient waterflooding operation is an
optimal production setting, most especially with respect to the amount of water involved. This research
work has been carried out to develop a model that can be used to maximize oil recovery and minimize water
production with the least amount and number of waterflood variables in order to minimize the secondary
recovery investment cost. The gradient-based approach to optimize the production and net present value
(NPV) from a waterflood reservoir using the flow rates or bottom hole pressures of the production wells
as the controlling factors with the use of smart well technology was applied. In this approach, a variant of
the optimal switching time technique was used in the optimization process to equalize the arrival times of
the waterfront at multiple producers, thereby increasing the cumulative oil production. The optimization
procedure involved maximizing the objective function (NPV) by adjusting a set of manipulated variables
(flow rates). The optimal pressure profile of the waterflood scenario that gave the maximum NPV was
obtained as the solution to the waterflood problem. The proposed optimization methodology was applied to
a waterflood process carried out on a reservoir field developed by a five-spot recovery design in the Niger
Delta area of Nigeria, which was used as a case study. The forward run was carried out with a commercial
reservoir oil simulator. The results of the waterflood optimization revealed that an increase in the net present
value of up to 9.7% and an increase in cumulative production of up to 30% from the base case could be
achieved.
Key Words: Hydrocarbon reservoir, waterflooding, optimization, net present value, commercial reservoir
simulator.
Introduction
Waterflooding refers to the use of water that is injected into a hydrocarbon reservoir using strategically
placed injector wells to maintain pressure and increase oil production. Water injection wells can be found
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Assessment and Optimization of Waterflooding Performance in a
Hydrocarbon Reservoir
2
SPE-207114-MS
i.
ii.
iii.
iv.
abundance of water,
low cost of treatment relative to other injection fluids,
ease of pumping water into a formation, and
water displacement efficiency.
Significant number of prominent oil fields are matured, and the number of new discoveries is declining;
it is imperative to optimize secondary oil recovery processes (Nwaozo, 2006). It has been reported that
waterflooding operations can lead to the recovery of about one-third of the original oil in place (OOIP),
leaving behind about two-thirds (Meshioye et al.,2010). At a certain point during waterflooding operations,
it becomes uneconomical to continue such operations because the cost of injecting, removing and disposing
water exceeds the net income generated from the oil production, thus the waterflooding has to be stopped.
However, some wells are still considered economical even at a watercut of up to 99% (Arenas and Dolle,
2003).
Globally, large amounts of hydrocarbon volumes are found in reservoir systems associated with high
shale volumes. These shales create discontinuities within the reservoir units. The presence of flow barriers
caused by these shales and variations in directional permeability across the reservoir strongly affect
the drainage patterns and the sweep efficiency of water injection processes (Awejori, 2010). Therefore,
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
both on- and offshore to enhance oil recovery from an existing reservoir. The use of water to increase oil
production is categorized as secondary recovery and sometimes follows primary production. The primary
production uses the reservoir's natural energy to produce oil. Other oil recovery methods known as enhanced
oil recovery (EOR) techniques are utilised when there is a need to reduce the oil-water interfacial tension
and/or reduce the mobility ratio of the fluids within the reservoir system. Decreasing the oil-water interfacial
tension results in an increase in the capillary number, and the mobility of the injectant may be reduced
by increasing water viscosity, reducing oil viscosity, reducing water permeability or all of the above, thus
increasing oil recovery and extending the productive life of an otherwise depleted and uneconomic oil field
(Craig, 1971).
Waterflooding is the most widely used secondary recovery mechanism, applicable even before energy
is exhausted in a reservoir. It has been recognized since 1880 that the injection of water into an oilbearing formation has the potential of improving oil recovery (Carll, 1880). However, waterflooding did not
experience field-wide application until the 1930s when several injection projects began (Fettke, 1938), and
it was not until the early 1950s that the current boom in waterflooding application began (Grema, 2013).
Waterflooding operations are said to be responsible for high oil production rates in mature oil fields in the
USA and Canada (Craig, 1971). Around 50% of the world's known oil reserves are in carbonate reservoirs
whose primary recovery mechanisms yield low recovery factors. When waterflooding a carbonate reservoir,
factors that affect any type of recovery, which can determine the success of oil recovery, must be considered.
Those factors include matrix permeability, wettability, fracture intensity, rock properties, fluid properties,
contact angle, porosity, etc. (OIT, 2018).
Over the years, various EOR techniques have been developed in an effort to extract crude oil from an oil
field that could not be extracted otherwise. According to the US Department of Energy (2018), there are three
primary techniques for EOR that have been found to be commercially successful to varying degrees. These
techniques offer prospects for ultimately producing 30 to 60 percent, or more, of the reservoir's original oil
in place. The three main EOR techniques are thermal recovery, microbial and chemical injection. Each of
these techniques have been hampered by its relatively high cost and, in some cases, unpredictability of its
effectiveness (Brouwer, 2004).
Despite the emergence of these EOR techniques, waterflooding still enjoys widespread applicability in
the petroleum industry. The main reasons why waterflooding is the most successful and most widely used
oil recovery process are (Craig, 1971; Willhite, 1986):
SPE-207114-MS
3
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
to accurately estimate the efficiency of a waterflooding process, an assessment of the impact of sand
discontinuities or connectivity in these reservoirs is required for realistic performance predictions of the
schemes and estimation of associated confidence limits. This is particularly important during the predevelopmental stage when major investment decisions are to be made on the basis of a limited number of
exploration wells (Handyside et al.,1992).
In matured waterfloods, oil is usually co-produced with large volumes of water (water cut larger than
75%). However, a significant amount of unswept oil remains in the reservoir as floods are operated at suboptimal conditions over many years, particularly as the oil production gradually declines over time and the
incentive for investments wanes. Oil production may be enhanced significantly with minimal infrastructure
investment simply by changing the injection/production rates to redirect the water to previously unswept
or poorly swept regions of the reservoir (Ogbeiwi, 2016).
The appeal of a reservoir simulation model stems from providing a single platform where different types
of data, such as geological, geophysical, reservoir fluid and rock properties, etc., are integrated in a physical
model and used for performance predictions. When properly calibrated and verified by alternative methods,
the reservoir simulation prediction can be reasonably accurate. However, despite offering many advantages,
reservoir simulation models have limitations. For instance, for a full-field study, both large time steps and
grid-block sizes often used for practical turnaround time for a single run causes loss of temporal and spatial
details. Also, reservoir simulators often fail to account for dynamic and finer scale well conditions (damage,
plugging, fracturing, etc.). These can lead to inaccurate prediction of performance and optimistic forecasts,
especially for secondary and tertiary recoveries. The three main steps of a reservoir simulation process have
been found to be constructing a dynamic reservoir model, history matching and production forecast and
performance prediction (Asadollahi, 2012).
From the information obtained in the literature, Lee and Aronofsky (1958)attempted to resolve production
optimization problems involving reservoir modelling with time. Their study was geared towards applying
linear programming procedures to oil production scheduling problems. The problem was to determine
an oil production schedule from 5 different wells that would give maximum profit over an eight-year
period. The constraints placed on the individual reservoir production rate of the wells included well
pressures and pipeline capacity. The problem was solved using constant well interference coefficients as
a substitute for a real reservoir simulation model. Wattenbarger (1969)further improved this study with
the use of real reservoir simulation models for estimating the well interference coefficients. He developed
a means of maximizing withdrawals from a natural gas storage reservoir and proposed a method for
optimizing the withdrawal schedule problem using linear programming method. The withdrawal schedule
was optimized in the sense that no discretized withdrawal schedule could be specified for the finite difference
model that would give greater total seasonal production while still meeting the constraints placed on the
problem. Asheim (1988)developed an approach that used only the control variables explicitly for numerical
optimization. He presented a method for numerical optimization of the net present value of a natural water
drive and water drive by injection. This method used a real two-phase reservoir simulator to calculate
the net present value of a waterflooding scheme. In this case, the variables that were subjected to control
were the well flow rates. The waterflooding scheme that maximized the net present value was numerically
obtained by combining reservoir simulation with control theory practices of implicit differentiation. He was
able to achieve improved sweep efficiency and delayed water breakthrough by dynamic control of the well
flow rates. For the reservoir models considered, there was a net present value increment of up to 11%.
Brouwer et al.(2001)studied the optimization of water flooding with fully penetrating, smart horizontal wells
in 2dimensional reservoirs with simple, large-scale heterogeneities. In the work, optimal control theory
was used as an optimization algorithm for valve settings in smart wells. The goal was to maximize the
recovery of the waterflooding process over a period of time. They investigated the static optimization of
waterflooding with smart wells and discovered significant improvements from simple reservoir models.
4
SPE-207114-MS
Methodology
In order to assess and investigate the influence of permeability anisotropy on waterflood performance and
production optimization in a hydrocarbon reservoir, an analysis of a case study on waterflood optimization
research was carried out. The case study was used to evaluate several waterflood optimization scenarios.
Thereafter, the approaches used for optimizing the waterflooding operation conducted in this research were
highlighted.
Permeability Anisotropy Analysis
The step-by-step approaches of how the analyses of permeability anisotropy of the studies were carried out
are as highlighted thus.
Analysing Permeability Anisotropy of the Research. The data for this case study was obtained from a
section of the Sanga Reservoir - 1 (Figures 1 - 3). The objective was to use the properties of this formation
to analyse various parameters that influence waterflood optimization in any given reservoir, such as kV/kH
ratios, and zones of injection and production.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
However, it was observed that more improvements could be achieved by dynamic optimization of the
production and injections. In a later study, Brouwer and Jansen (2004)addressed this same problem using
dynamic optimization in which the inflow control valves in the wells were allowed to vary during the
waterflooding process. The waterflood was improved by changing the well profiles according to some
simple algorithms that move flow paths away from the high permeability zones in order to delay water breakthrough. This was achieved by calculating the productivity index (PI) for each segment. For each well, the
segments with the higher PI were shut-in and the rates were equally distributed among the other segments
that were open in order to maintain the production rates. This process was repeated until the optimum flow
profile was obtained, and this was found to occur when the ultimate oil recovery from a successive step was
lower than that obtained with the preceding flow profile. Lorentzen et al.(2006) also carried out a study on
the dynamic optimization of waterflooding using an approach different from those previously stated. The
authors carried out the optimization by controlling the chokes to maximize cumulative oil production. This
new approach used the ensemble Kalman filter as an optimization routine. In the work, they demonstrated
how to use the ensemble Kalman filter as an optimization routine on a simple 5-layer reservoir with varying
permeability values. Their methodology avoided the use of the optimal control theory since no adjoint
equations were needed and the model equations were treated as a black box. Nwaozo (2006) improved on the
work of Lorentzen et al.(2006) by presenting a new production optimization algorithm, which optimized the
controls to the maximum possible net present value by maximizing an objective function. His methodology
took its concept from the ensemble Kalman filter for continuous model update and it was successfully
applied to various heterogeneous waterflood reservoir models. The optimization process showed remarkable
improvement in net present value of up to 9% from the initial base case as well as an improvement of
cumulative production of up to 8% from the base case. Also, the water saturation at breakthrough was
observed to be more uniformly distributed across the reservoir after the optimization process as compared
with the unoptimized case.
The thrust of this research is on gaining a clearer understanding of waterflooding processes, the factors
that affect the efficiency of water injection and developing a dynamic model that will optimize the
performance of waterflooding processes in a hydrocarbon reservoir. Therefore, this study was focused on
examining waterflooding optimization procedures in order to develop a model that would optimize oil
production under waterflooding conditions. The proposed model explored a gradient-based approach that
was applied to a field case study under waterflooding conditions for a feedback strategy that resulted in
optimal operation for increasing oil recovery significantly.
SPE-207114-MS
5
Figure 2—Permeability distribution
Figure 3—Porosity distribution of the Sanga Reservoir
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 1—Well configuration and original oil saturation profile of the Sanga Reservoir
6
SPE-207114-MS
Four kV/kH ratios were selected and their effects on the waterflood performance were analysed using a
commercial reservoir simulation tool. These ratios were:
i.
ii.
iii.
iv.
kV/kH ratio of 0.001 (kV/kH _0.001)
kV/kH ratio of 0.01(kV/kH _0.01)
kV/kH ratio of 0.10 (kV/kH _0.1)
kV/kH ratio of 0.60(kV/kH _0.60)
Waterflood Optimization Procedure
A variant of the optimal switching time control technique was adapted to the problem of production
optimization in this study. The objective function maximized was the net present value (NPV), and the state
variables were fluid pressure and saturation while the control variables were the flow rates. The waterflood
optimization was aimed at increasing the cumulative oil production by determining the optimal pressure
profile of the reservoir model using the five-spot waterflood pattern. Five (5) smart wells were used for
the waterflood procedure (four producer wells and one injector well). The wells were equipped with three
inflow control valves (ICVs).
The waterflood optimization approach proposed in this study was applied to Sanga reservoir as a case
study using the data obtained. The reservoir was an undersaturated reservoir with a stock-tank-oil- initiallyin-place of 37.18 MMSTB. The dynamic reservoir properties for the T-1 reservoir are shown in Table 1.
Table 1—Dynamic properties of the Sanga Reservoir
Parameter
Value
Reservoir Pressure (Psia)
4271
Bubble Point Pb (Psia)
4021
Formation Volume Factor (Bo)
1.63
Gas Oil Ratio (GOR) Rsi (Mscf)
1.34
Datum Depth (ft)
8519.6
Oil Water Contact (OWC) (ft)
8610.27
Static Volumes (MMSTB)
37.48
Dynamic Volumes (MMSTB)
37.18
Difference (%)
1
Reservoir Modelling
A simplified version of the black oil formulation, a two-phase system containing only water and oil with
complete immiscibility, was utilized. This simplification eliminated the phase equilibrium relation due to
the solubility of gas in the oil phase, and this resulted in reduction of the computational cost per simulation.
The simplified description of the reservoir fluids was sufficient for showing the production optimization
technique used in practice.
The constructed model was in grid unit of feet with the following grid axes with respect to map
coordinates and dimensions: 161 × 70 × 19 and 214130 grid cells. The specifications for the reservoir model
are shown in Table 2.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
The base case of the kV/kH ratios was 0.6. Water was injected in zones 6 - 8, and the producers were
completed in zones 4 - 6.
SPE-207114-MS
7
Table 2—Specifications for modelling the Sanga Reservoir
Reservoir
Sanga
Grid Dimensions
Number of
Active Cells
I
J
K
161
70
19
Layering
Phase and
Fluid Options
Geometry
Options
Solution
Method
twophasesystems
214130
1 to 19
Oil and Water
Corner Point
Fully Implicit
Reservoir Simulation
A reservoir simulation tool specialised in black oil modelling (Petrofaq, 2018) was employed for the
reservoir simulation workflow of this study. The simulation and optimization of the reservoir under
waterflooding conditions were carried out using the steps illustrated as given in the reservoir simulation
workflow shown in Figure 4.
Figure 4—Reservoir simulation workflow
At the pre-simulation process, fluid characterization and PVT analysis were carried out, and the basic
features of the reservoir under consideration were delineated to characterize parameters such as viscosity,
density, API gravity, rock properties (porosity, relative permeability, compressibility), oil and gas production
rate and pressure of reservoir. The reservoir data gathered were analysed and evaluated based on the
available reservoir information. Furthermore, the quality of the data was checked for consistency before
being linked to the static/geological model.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Simulation start date was defined for the cases depending on the production start date of the well. Grid
dimensions (in x, y and z directions) and boundary conditions were specified. Cartesian grid and corner
point grid geometry options were also chosen for more accurate reservoir modelling. Reservoir fluid phases
present (water and oil) were defined. A fully implicit solution method was used for all the runs to guarantee
convergence of the solution type.
8
SPE-207114-MS
Results and Discussion
Results of Research Analysis
The results showing the effects of variations in the ratio of vertical-to-horizontal permeability on waterflood
performance are presented in the following subsections. The presented pattern scenario is the regular fivespot pattern. The results for the direct-line and staggered-line drive are also presented and analysed.
Effects of kV/kH ratios on cumulative oil production (FOPT). Figure 5 shows the plot of cumulative
oil production versus time for the five-spot waterflood considering four kV/kH ratios of 0.001, 0.01, 0.1
and 0.6. The results showed that the cumulative oil production improved with increasing kV/kH ratio. This
was found to be a result of the fact that the sweep efficiency of the waterflood improved with better kV/
kH ratio owing to the improved crossflow/fluid transmissibility between layers from non-depleted zones to
already depleted zones within the reservoir. Figure 6 shows the plot of cumulative oil production versus
time for the direct-line drive waterflood considering the four kV/kH ratios when simulated for thirteen years
(3 years of primary depletion and 10 years of waterflood). According to the results, which were found to be
similar to those obtained from the five-spot pattern, the cumulative oil production was observed to increase
with increasing kV/kH ratio. Although the total production rate from the wells was fixed at 1000 stb/day
(500 stb/day from each well) and total injection rate at 2800 stb/day (700 stb/day for each injector). It was
observed that the cumulative production at the end of simulation for all the kV/kH cases analysed was higher
compared to that obtained from the five-spot scenario (with one producer producing at 1000 stb/day and
four injectors injecting at 700 stb/day). This was because the two producer wells (for the direct-line drive)
were observed to be advantageous over one producer well (for the 5-spot) based on the availability of more
zones of the reservoir for drainage by the producers. Figure 7 shows the performances for the staggeredline drive waterflood considering the four kV/kH ratios simulated for thirteen years. In this case, the wells
were set in the same manner as the direct-line drive pattern with the same injection and production rate.
The results, in this case too, were found to be similar to those obtained from the preceding patterns, which
revealed an improvement in the cumulative oil production with increasing kV/kH ratio. Also, the staggeredline drive pattern could be observed to give the highest cumulative recovery for all kV/kH ratios considered
from the previous waterflood. This was due to the lateral displacement of the injectors to the producers that
ensured that larger areas of the reservoir were contacted by the injected water and, hence, oil was better
swept towards the producers.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Economic Analysis
The objective function of the economic analysis was the net present value of the waterflood operation for
a given production period of 30 years. The goal was to maximize the net present value over the life of the
reservoir, and this was achieved by adjusting a set of controls (bottom hole pressures (BHPs) or flow rates).
In economic analysis, the present value of money compares the value of a certain amount of money today
to the value of that same amount in the future and vice versa, taking into consideration conditions such as
inflation. Given an investment opportunity, net present value is used as a decision-making tool to analyse
the profitability of a proposed project and to make key decisions with regards to capital budgeting. It is
sensitive to the future cash inflows that an investment or project will yield.
SPE-207114-MS
9
Figure 6—For the direct-line drive waterflood
Figure 7—For the staggered-line drive Waterflood
Figure 8—For different kV/kH ratios for the 5- spot pattern
Effects of kV/kH ratios on the field oil producing rate (FOPR). Figures 8 to 10 present the plots of the
oil producing rate of the field against time for the five-spot, direct-line drive and the staggered line-drive
waterflood projects, respectively, considering the four kV/kH ratios. Generally, it can be observed that cases
with lower kV/kH ratios had lower oil production rates in comparison to cases with higher kV/kH ratios. Thus,
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 5—For the 5-spot pattern
10
SPE-207114-MS
oil production rates are reduced. In contrast, cases with higher kV/kH ratios showed higher oil production
rates in comparison to cases with lower kV/kH ratios. This can be attributed to good lateral transmissibility
between the layers as a result of good communication between zones in the reservoir.
Figure 10—kV/kH scenarios for the staggered-line drive
Effects of ky/kH ratios on reservoir pressure (FPR). Figure 11 is a plot of average reservoir pressure
against time for the five-spot waterflood when four kV/kH ratios were considered. The results showed that
higher kV/kH ratios led to lower pressure decline. The case of kV/kH = 0.60 was found to have the lowest
pressure decline while that of kV/kH = 0.001 had the highest one throughout the period of simulation. This
was as a result of poor communication between the zones for reservoirs with low kV/kH ratios; thus, lower
pressure maintenance was experienced. Recalling that water injection normally moves preferentially in the
vertical upwards direction due to gravity. A high kV/kH ratio implies good vertical communication between
reservoir zones. Hence, the easier movement of injected water vertically was observed to occur, and this
led to better pressure maintenance.
Similar responses were observed for the direct line-drive and the staggered line-drive scenarios shown in
Figures 12 and 13, respectively. Figure 12 presents a dynamic response of field pressure for the direct-line
drive waterflood for the four kV/kH ratios considered. The results revealed that the trend in reservoir pressure
was lower than that of the five-spot waterflood. This was due to the fact that even if the waterfloods have
an equal number of injectors (injecting at the same total field injection rate of 2800 stb/day), the number of
producers were different (one for the five-spot and two for the direct-line drive).
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 9—kV/kH scenarios for the direct-line drive
SPE-207114-MS
11
Figure 12—kV/kH scenarios for the direct-line drive
Figure 13—kV/kH scenarios for the staggered-line drive
Analysis of Figure 13 revealed that the trend in reservoir pressure after waterflooding was
correspondingly higher than those of the previous waterflood patterns considered. This could be attributed
to the lateral orientation of the injectors to the producers. This was found to allow effective displacement
of injected water. Thus, better pressure maintenance was observed.
Effects of kV/kH ratios on field water-cut (FWCT). Figures 14 to 17 present the plots of the field watercut versus time of all the kV/kH cases considered for the five-spot, direct-line drive, and staggered-line drive
waterfloods, respectively.
Figure 14—kV/kH scenarios for the five-spot drive pattern
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 11—kV/kH scenarios of the five-spot waterflood
12
SPE-207114-MS
Figure 15—kV/kH scenarios for the direct-line drive
It was discovered from the results for all the waterflood scenarios analysed that all kV/kH cases showed
no significant water-breakthrough prior to waterflooding until around five (5) years of production. The
trend of field water-cut for all the scenarios of waterflood considered was observed to be similar to the
highest water-cut of less than 80% at the end of the simulation. This occurred because the number of
injectors and the injection rate for each scenario was the same. Furthermore, it was observed that for
all the scenarios considered, increasing kV/kH ratios led to high water-cut. This was because increased
communication between the zones enhanced the sweep efficiency in all zones leading to an increase in
water production at the producers and, subsequently, field water-cut. Poor communication between zones
led to areas with very low kV/kH ratios (see cases kV/kH _0.001 and kV/kH_0.01) that led to reduced water
production.
Analysis of Waterflood Performance
The algorithms developed for waterflood simulations conducted on the reservoir were tested with various
scenarios under eight (8) different injection rates, which are denoted as cases 1 to 8.
Base Case. The reservoir was put on production with four oil producer wells to observe its performance
under natural production. The simulation was carried out for thirty (30) years with a start-up rate of 1200
stb/day.
Figure 17 shows the production profile of the reservoir after 30 years of natural production. For this case,
primary production was done using reservoir depletion mechanism and water injection was not considered.
This was done to ascertain the best time to begin waterflooding. At the start of production, the reservoir's
STOIIP was 37.18 MMSTB. The results of the simulation run showed an estimated oil recovery of 8.72
MMSTB with a recovery factor of 23.45%. History matching data gave an NPV of $133,000,000 for the
project.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 16—kV/kH scenarios for the staggered-line drive
SPE-207114-MS
13
The oil saturation profile before and after production for the base case are given in Figures 18 and 19,
respectively.
Figure 18—Oil saturation profile before production for the Base Case
Figure 19—Oil saturation profile after production for the Base Case
Case 1. The waterflood optimization simulation was carried out for thirty (30) years. For the five-spot
pattern, the four producer wells formed a square with the injector well in the middle. The water injection
rate for this case was set at 500 bbl/day. Figure 20 presents the production profile for the reservoir after
water injection at 500 bbl/day.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 17—Graph showing the production profile for the Base Case
14
SPE-207114-MS
The results from Figure 20 showed an estimated oil production rate of 9.12 MMSTB, with a recovery
factor of 25% from the original oil in place. In this case, the reservoir pressure can be observed to decline
rapidly and water breakthrough did not occur.
Case 2. The waterflood optimization simulation was carried out for thirty (30) years. The water injection
rate for this case was set at 1000 bbl/day. Figure 21 shows a plot of the production profile for the reservoir
after water injection at 1000 bbl/day. The results for this case showed a slight increase in the estimated oil
production rate with a value of 9.49 MMSTB, a recovery factor of 0.26 from the original oil in place, and a
pressure point below 1000 psia. In a similar manner to Case 1, no water breakthrough was found to occur.
Figure 21—Graph showing the production profile for water injection at 1000 bbl/day
Case 3. The waterflood optimization simulation was carried out for thirty (30) years. The water injection
rate for this case was set at 1500 bbl/day. Figures 22 through to 24 present the production profile, oil
saturation before waterflooding and oil saturation after waterflooding, respectively, for the reservoir after
water injection at 1500 bbl/day. The results from the production profile showed an estimated oil production
rate of 9.72 MMSTB, which was slightly different from the value obtained from Case 2 with a recovery
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 20—Graph showing the production profile for water injection at 500 bbl/day
SPE-207114-MS
15
factor of 26.14% from the original oil in place and a very low pressure point of 1,200 psia. Once again, no
water breakthrough was observed to occur in this case.
Figure 23—Oil saturation profile befor water injection at 1500 bbl/day
Figure 24—Oil saturation profile after water injection at 1500 bbl/day
Other cases were also tried, and the summary of all of them are given in Table 3.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
Figure 22—Graph showing the production profile for water injection at 1500 bbl/day
16
SPE-207114-MS
Table 3—Summary of total oil production from all the cases considered
Injection Rates (Bbl/Day)
Oil Production
Total (MMSTB)
Water Cut (%)
7000
7.63
92
20.52
6000
7.53
89
20.25
5000
11.02
93.8
29.64
4000
11.38
93.2
30.61
3000
11.35
92.7
1500
9.72
0
26.14
1000
9.49
0
25.52
500
9.12
0
24.53
Base Case
8.72
0
23.45
STOIIP
Recovery Factor (%)
30.16
Net Present Value (NPV) Analysis. The results of the net present value (NPV) of the process are given in
Figure 25. According to the results shown in the figure, the NPV after the optimization process was found
to increase. The plot shows a percentage increase in the net present value ranging between approximately
2.2% and 9.8% after the optimization.
Figure 25—Graph of NPV for all realizations of pressure profiles before and after optimization
Table 4 presents a summary of the results for oil recovery and NPV optimization obtained from the
reference case and the optimized case for the reservoir. It was clear from the results shown in Table 4 that
the process was able to be maximized.
Table 4—Summary of waterflooding optimization results for the reservoir
BASE CASE
OPTIMIZED
OPTIMIZATION EFFECT
Cum. Oil Production
×106 STB
NPV ($Mil)
Cum. Oil Production
×106 STB
NPV ($Mil)
Increase in Cum.
Oil Production (%)
Increase in NPV (%)
8.72
133
11.35
146
30.16
9.77
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
37.18
SPE-207114-MS
17
Conclusion
1. increasing water injection rates could lead to enhance reservoir pressure maintenance that resulted
in improved oil recovery but water injection at very high rates could lead to an increase in water
production, which would reduce field sweep efficiency (water by-passing oil) and negatively impact
the profitability of the waterflood project,
2. during waterflood projects, certain parameters (injection rate, water density, up-dip flow displacement,
well spacing ratio, etc.) could be used to decrease water production and increase oil recovery, and this
was achieved by reducing water by-passing,
3. variations in the lateral and vertical heterogeneity of a reservoir significantly impacted the injection
and production rate of an oil-bearing system, and the presence of a high kV/kH ratio across a reservoir
showed a lesser likelihood of vertical cross-flow between layers in the reservoir, which led to better
sweep in highly permeable, less heterogeneous zones, thus resulting in better pressure maintenance
and improved field oil recovery,
4. the application of the optimal switching time optimization algorithm to waterflooding processes could
be used to enhance sweep efficiency by equalizing the arrival times of the water front at multiple
producers, thereby increasing the cumulative oil production, and
5. the optimal switching time optimization used streamlines to efficiently and analytically compute the
sensitivity of the arrival times with respect to well injection rates.
Acknowledgment
Special thanks go to Aare Afe Babalola, LL.B, FFPA, FNIALS, FCIArb, LL.D, SAN, OFR, CON - The
Founder and President, and the Management of Afe Babalola University, Ado-Ekiti, Ekiti State, Nigeria for
providing a very conducive environment that enabled the accomplishment of this research work.
References
1.
2.
3.
4.
5.
6.
Arenas, A., Dolle, N., 2003. Smart Waterflooding Tight Fractured Reservoirs Using Inflow
Control Valves. SPE paper 84193 presented at the SPE Annual Technical Conference and
Exhibition, Denver, Colorado, 5 - 8 October.
Asadollahi, M.2012. Waterflooding Optimization for Improved Reservoir Management. Ph.D.
Thesis. NTNU, Trondheim, Norway.
Asheim, H.1988. Maximization of Water Sweep Efficiency by Controlling Production and
Injection Rates. Paper SPE 18365 presented at the SPE European Petroleum Conference, London,
UK, October 16 - 19, 1988.
Awejori, G. A.2010. Integrated Petrophysical Evaluation of Turbiditic Sands in Niger Delta
Basin. M.Sc. Thesis, Unpublished, AUST, December, 2010.
Brouwer, D. R., Jansen, J. D.2004. Dynamic Optimization of Waterflooding with Smart Wells
Using Optimal Control Theory. SPE Journal, vol. 9, no. 4, pg. 391–402.
Brouwer, D. R., Jansen, J. D., van der Starre, S., van Kruijsdijk, C. P. J. W., Berentsen, C. W.
J.2001. Recovery Increase Through Water Flooding with Smart Well Technology. In Proceedings
of SPE European Formation Damage Conference, The Hague, Netherlands. Pg. 1–10.
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
The results obtained from the application of the waterflood optimization algorithm developed on a reservoir
in the Niger Delta region of Nigeria has revealed that the optimization process was able to show remarkable
improvement in net present value of up to 9.77% from the initial base case as well as an improvement
of cumulative oil production of up to 30.16% from the base case. These results show that the proposed
waterflood optimization method can be used to develop an entire field, extend the life of an oil reservoir,
and increase oil revenue significantly. Furthermore, it was discovered from the research that:
18
SPE-207114-MS
7.
8.
9.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
View publication stats
Downloaded from http://onepetro.org/SPENAIC/proceedings-pdf/21NAIC/2-21NAIC/D021S007R001/2457423/spe-207114-ms.pdf by Adekunle ADENIYI on 25 September 2021
10.
Brouwer, D. R., Naevdal, G., Jansen, J.D., Vefring, E.H., van Kruijsdijk, C.P.J.W.2004. Improved
Reservoir Management Through Optimal Control and Continuous Model Updating. SPE 90149,
SPE Annual Technical Conference and Exhibition, 26 - 29 September, 2004.
Carll, J.F.1880. The Geology of the OH Regions of Warren, Venango, Clarion, and Butler
Counties, Pennsylvania. Second Geological Survey of Pennsylvania III. Pg. 1875–1879.
Craig, F. F., Jr.1971. The Reservoir Engineering Aspects of Waterflooding. Monograph Series,
SPE, Dallas, Texas.
Fettke, C. R.1938. Bradford Oil Field, Pennsylvania and New York. Pennsylvania Geological
Survey, 4th Series M-21.
Grema, A. S., Cao, Y.2013. Optimization of Petroleum Reservoir Waterflooding Using Receding
Horizon Approach. In Proceedings of the 8th IEEE Conference on Industrial Electronics and
Applications, IEEE, Melbourne, Australia, pg. 397–402.
Handyside, D. O., Karaoguz, O. K., Deskin, R. H., Mattson, G. A.1992. A Practical Application
of Stochastic Modeling Techniques for Turbidite Reservoirs. SPE paper 24892 presented at the
67th Annual Technical Conference and Exhibition of the Society of Petroleum Engineersheld in
Washington, DC, October 4 - 7, 1992.
Lee, A. S., and Aronofsky, J. S.(1958). A linear programming model for schedulingcrude oil
production. Journal of Petroleum Technology, 10(7), pp. 51–54.
LorentzenR. J., BergM. A., NaevdalG. and VefringE. H.2006. A New Approach for Dynamic
Optimization of Water Flooding Problems. Paper SPE 99690, prepared for presentation at the
SPE Intelligent Energy Conference and Exhibitionheld in Amsterdam, The Netherlands, 11-13
April 2006.
Nwaozo, J.2006. Dynamic Optimization of a Water Flood Reservoir. M Sc. Thesis, University of
Oklahoma Graduate College, Norman, Oklahoma, USA.
Ogbeiwi, P.2016. An Approach to Waterflood Optimization: A Case Study. M.Sc. Thesis,
Unpublished. AUST, Dept. of Petroleum Engineering, July, 2016. Pg. 14–59.
Oil Industry Training (OIT). 2018. Waterflooding in Carbonate Reservoirs. [Online]
Petrofaq. 2018. Eclipse [Online]. Available at: https://petrofaq.org/wiki/ECLIPSE
U.S. Department of Energy (Energy). 2018. Enhanced Oil Recovery. [Online]. Available at:
https://www.doe.gov
Wattenbarger, R. A.1969. Maximizing Seasonal Withdrawals from Gas Storage Reservoirs. Paper
SPE 2406 presented at SPE 44th Annual Fall Meetingin Denver, Colorado, Sept. 28 - Oct 1,
1969.
Wilhite, G.P.1986. Waterflooding. Textbook Series, SPE, Dallas.