Water flooding is a significant recovery process used for secondary recovery and pressure maintenance to increase the life of a producing well. This is a reservoir management practice for maximize the efficiency and optimize the Hydrocarbon recovery. Different reservoir characteristics are the cause of less understanding about the selection of water flooding design and to maximize the efficiency
Reservoir simulation model are used to design the different water flooding patterns in order to maximize the volumetric sweep efficiency. It was shown in early studies that heterogeneity of reservoirs highly affects the sweep efficiency regardless of designs of water flooding patterns.
In this project, a reservoir simulation tool (ECLIPSE 100) and the basic and critical reservoir engineering strategies are used to design water flooding models. The major objectives were to follow a pattern-based approach to focus on injection patterns to select the most optimum design or pattern that gives maximum recovery of oil in minimum period of time.
CHAPTER 01
INTRODUCTION
Natural drive mechanisms do not have enough capability to recover a high percent of total in place. That oil has to be recovered by using different secondary methods. The water flooding is the secondary recovery technique that cannot be successful unless a perfect design is done. The maximum recovery of hydrocarbons is the most critical problem during production. The percentage of recovery depends upon the water flooding pattern, well spacing, and injection and production rates. The proper selection of these factors can only be done through simulator prior to the operations.
Water flooding is a process which is used to maintain reservoir pressure by injecting water in the reservoir and to displace the oil that is not able to move naturally to the surface. Water flooding is a successful and the oldest secondary recovery method which is first tried in 1965. Water flooding operation is used to maintain or increase the reservoir pressure, to displace the residual oil saturation, to increase ultimate oil recovery and to increase the oil production rate. The major objective of water flooding is to increase oil recovery and to increase production life of the wells.
Well Patterns
5-SPOT
A pattern in which four injection wells are placed at the four corners of the reservoir and one producer is placed in the center of the reservoir this pattern is known as 5-spot pattern
The Oil Recovery for the above pattern is 5%, Field Water Cut is 93% and Field Oil Production Rate is 43 STB/Day.
5-SPOT INVERTED
5-Spot inverted is opposite to 5-Spot in which four injection wells are replaced by four producer wells whereas one producer well is replaced by one injection well
The Oil Recovery for this reservoir is 23%; Field Water Cut is 90% and Field Oil Production Rate Is 73 STB/Day.
7-SPOT
7-spot consists of 6 injectors at the corners of the hexagon with a single producer at the center. The Oil Recovery for this pattern is 2.3%, Field Water Cut is 13% and Filed Oil Production Rate is 536 STB/day.
7-SPOT INVERTED
7-spot consists of 6 Producers at the corners of the hexagon with a single Injector at the center. The Oil Recovery for this pattern is 13%, Field Water Cut is 91% and Field Oil Production Rate is 367 STB/day.
STAGGERED LINE DRIVE
Staggered line drive similar to the direct line but the producers and injectors are not opposite to one another. The line of injectors and producers oppose each other but the number of producers is greater than injectors.
The Oil Recovery for this pattern is 23%, Field Water Cut is 89% and Field Oil Production Rate is 158 STB/day.
Recovery Efficiency
The percentage of the initial in-place hydrocarbon that can be recovered in the project. The overall recovery efficiency is the union of three individual efficiency factors.
Recovery Factor (Efficiency) = (ED x EA x EV)
Where,
ED = Displacement Efficiency, EA = Aerial sweep efficiency, EV = Vertical sweep efficiency
Displacement Efficiency
Displacement efficiency is the amount of oil that has been forced out from the swept zone at any given time.
Displacement efficiency equation is given by,
E = (Voi-Vor) / Voi
Voi = Volume of oil at start of flood
Vor = Volume of oil remaining after flood
Aerial Sweep Efficiency
The fractional area from which reservoir fluid is displaced by the injected fluid is known as aerial sweep efficiency. It is effected by,
• Formation dip angle
• Formation azimuth
• Fluid mobility
• Aerial heterogeneity
• Total volume of fluid injected
Vertical Sweep Efficiency
The fractional vertical section of the pay zone that is contacted by the injected fluid is known as vertical sweep efficiency.
It depends on parameters such as,
• Mobility ratio
• Injected fluid volume
• Vertical heterogeneity
Figure 1: Tarek Ahmad – Reservoir Engineering Handbook, 3E, pg-929, Flood patterns. (Permission to publish by the Society of Petroleum Engineers.)
Corey’s Function
In 1954 Corey derived a simple mathematical expression for making the relative permeability data of the gas-oil system.
Corey’s Function for Relative Permeability Curves:
To derive the Relative permeabilities of Oil & Water at different Saturations, a technique called as Corey’s Function is used.
By using the Formulae we are able to get the Relative Permeabilities of oil and water.
k_rw=(k_rw ) S_or [(S_w-S_wi)/(1-S_wi-S_or )]^(n_w ) k_ro=(k_ro ) S_wi [(1-S_w-S_or)/(1-S_wi-S_or )]^(n_o )
Where,
Krw = Relative Permeability of water
(Krw)Sor = End point Relative permeability of water
(Kro)Swi = End point Relative Permeability of Oil
Sw = Saturation of water
Swi = Connate / Initial water Saturation
Sor = Irreducible Oil Saturation
No = Oil Corey’s exponents
Nw = Water Corey’s exponents
CHAPTER 02
LITERATURE REVIEW
Dykstra and Parson (1950) have introduced the effects of different factors i.e. porosity, permeability, viscosity, initial saturations, anisotropic ratio on the recovery of oil during water flooding. They described that the recovery of oil is maximum when initial saturation of oil is greater than 70%. (Dykstra & Parson, 1950)
J. S. Afronosky (1952) conducted Numerical computations and Potentiometric model studies to describe the mobility ratio effects on the recovery efficiencies at the time of water injection in oil reservoirs. The geometry of direct-line drive well pattern was used. The studies were conducted for an idealized system and it was found that the property that has maxim
um dependency on mobility ratio is sweep efficiency. (Aronofsky, 1952)
As reservoir is consisting on number of layers having different properties that effect the predicted performance of water fl
ooding. The author mentioned the effect of different properties on the predicted performance of water flooding through 5-spot pattern results. These properties include
How mobility ratio effect on the injectivity of fluid.
How mobility ratio and variation in permeability effect on volumetric efficiency.(F. F. Craig, 1972
Surendra P.singh et al, in (1982) described that the reservoirs with strong water drive, large gas caps and with good gravity drainage are unusual for water flooding process. He also illustrated that the horizontal permeability is usually greater than vertical permeability due to the grain orientation and the cementing material. (Singh, 1982)
In 2002, Mario Farías, et al, the purpose of this study is to analyze the dynamic skin near wellbore of injection and production wells by developing techniques using numerical simulation as a platform to critically analyze reservoir performance. Data for model construction and history matching is used by ongoing waterflooding on Gordon sandstone formation found in the Appalachian Basin, several correlations and assumptions. The results suggest that the skin damage increase with time of all injection wells due to the emulsion of injection water and various minerals along with the variation in matrix permeability. Determination of skin factor can be estimate from the change in flow rate of production and injection wells. Results from this case study concluded that the greater the efficiency of well stimulation near wellbore, the more the homogenous reservoir. (Mario Farías, 2002)
O. A. Bibars studied the water flooding project of EI Morgan field which was initiated by Gulf of Suez petroleum company (GUPCO) of Egypt. The peripheral water flooding was found to be the most economical pattern because of high heterogeneity and non-uniform well placements. The natural or primary recovery factor of oil was 25% of original in place volume of oil. The line drive pattern has increased recovery factor up to 40% of original in place of oil volume. (Bibars,2004)
In 2008, Russian Guliyev, et al, purpose of this study is to analyze the effect of mobility ratio on five-spot and staggered line drive waterflooding patterns in an isotropic, homogeneous and no initial gas saturation reservoir using simulation software Eclipse 100. Areal sweep efficiency v/s Mobility ratio is determined for five-spot and staggered line drive using different grid sizes of 60x30x1, 40x20x1, 20x10x1 & 200x100x1 models (aspect ratio of 0.75, 1, 1.25 & 1.75). Simulation results of Areal sweep efficiency at breakthrough v/s Craig mobility ratio at various aspect ratio (d/a) are presented in the form of graphs and indicate that due to the increment of mobility ratio areal sweep efficiency decreases– in line with experimental data – for all aspect ratios. (Guliyev, 2008)
A.Bakhiet et al, in (2009) showed that the direct line drive gives the maximum increase in recovery factor up to 39% of total oil in place compared to 24% by natural depletion in El-Faras field Egypt. Staggered line and direct line drives are the best well patterns for the reservoirs having low solution gas oil ratio and low water influx. (A.Bakhiet, 2009)
According to Amin Rezapour (2009), in order to increase the recovery factor, this task has been assigned to integrated and more structured approach called Closed Loop Reservoir Management (CloReM) furthermore This thesis presents a MPC methodology to improve CloReM performance. CloReM is a model based optimization algorithm to increase reservoir profitability using systems & control techniques. The pressures and saturations in the reservoir can be estimate if new production measurements and seismic data are available. These data update the reservoir models by using assimiliation algorithms. it also recomputed the injection rate at injection well and BHP. MPC (Model Predictive Control) is a control algorithm based on numerically solving an optimization problem at each step. (Amin Rezapour, Dec 2009)
Arsenevsky, Belyanushkina (2012) described the outcomes of a comparative analysis on different techniques (static and dynamic) for assuring the well allocation factors. The limitations of different methods were determined and estimated its performance for different water flooding patterns. They proposed to divide a model into grid blocks. (Belyanushkina, 2012)
B. Aminshahidy, et al, in (2013) compared gas injection with water injection in one of Iranian oil reservoir with two different patterns; five spot and Peripheral. Because the reservoir rock was water wet with very high initial gas saturation as compare to initial water saturation, gas injection in Five-spot was found as best result. (Aminshahidy, 2013)
In 2015, Ahmed Gsem Alkreem Abbas, et al, the purpose of this study is to maximize the recovery of oil by maintaining the pressure of reservoir and increasing sweep efficiency through water flooding in Fula North field, static and dynamic model of reservoir is first converted from Eclipse to CMG using ECLIPSE 100 converter. Four different cases have been analyzed in this study to prefer the optimized one. Case 1 is a base case in which no alteration in wells done and produces until 2021, Case 2 is a 5 spot pattern with all wells at production, Case 3 is an inverted 5 spot pattern with injection from the middle well and Case 4 is a normal 5 spot pattern with injection from 4 wells up to 2021. Sensitivity analysis of different cases suggests that Case 3 (4-wells producer & 1-well injector) gives high cumulative oil production and selected for further optimization, several injection rates on this pattern in tested, 1500 bbl/day has found the optimized injection rate. (Ahmed Gsem Alkreem Abbas, 2015)
In April 2017, 5-spot & 9-spot water flooding patterns on “Maesoon oil field” of Thailand are compared by using reservoir simulation through “ECLIPSE” software involving both technical and economic considerations. The result from this comparison found that a 9-spot water injection pattern gives more production than 5-spot. Also the economic analysis indicates that 9-spot injection patterns give maximum internal Rate of Return (IRR) and profit to investment ratio (PIR). (Chananchida Supaprom and Akkhapun Wannakomol, 2017)
CHAPTER 03
Problem Statement
Primary recovery leaves behind about 80% of the original oil in place (Thakur and Satter 1998; Cobb and Marek 1997; Taber et al. 1996). Majority portion of oil starts becoming residual when the natural pressure of the reservoir starts to deplete. In order to gain recovery, water flooding is used. Different types of water flooding patterns are used depending upon the type of reservoir, viscosity, wettability etc.
Research Objective
The objective of this work is to explore different water flooding patterns and evaluate the most favorable pattern in terms of recovering of oil.
Research Methodology
Simulation was used as a tool to interrogate the feasibility of achieving recovery improvements by applying the proposed idea. For optimization purposes, again simulation was used for scrutinizing the effect of different water flooding patterns on oil recovery.
Eight patterns were created with their inverted forms. The eight patterns include the following:
5-spot
Inverted 5 spot
7-spot
Inverted 7 spot
9-spot
Inverted 9 spot
Direct line drive
Staggered line drive
Data collection
Reservoir Rock Properties:
Porosity
Permeability
Water Saturation
Reservoir Condition Data:
Original Reservoir Temperature
Original Reservoir Pressure
Datum Depth
Res
ervoir Fluid Characteristics Data:
Oil gravity
Bubble point pressure (Psia)
Formation Volume Factor of water
Viscosity
Compressibility of water
Relative Permeability Curve
CHAPTER 04
SIMULATION WORK
Models
5-SPOT
A pattern in which four injection wells are placed at the four corners of the reservoir and one producer is placed in the center of the reservoir this pattern is known as 5-spot pattern
The Oil Recovery for the above pattern is 5%, Field Water Cut is 93% and Field Oil Production Rate is 43 STB/Day.
Figure 3: 3D view of 5-spot at 14% water cut.
5-SPOT INVERTED
A pattern in which four production wells placed at the corners of the reservoir and the injector well placed in the center is known as 5-spot inverted.
The Oil Recovery for this reservoir is 23%; Field Water Cut is 90% and Field Oil Production Rate Is 73 STB/Day.
7-SPOT
7-spot consists of 6 injectors at the corners of the hexagon with a single producer at the center. The Oil Recovery for this pattern is 2.3%, Field Water Cut is 13% and Filed Oil Production Rate is 536 STB/day.
7-SPOT INVERTED
7-spot consists of 6 Producers at the corners of the hexagon with a single Injector at the center. The Oil Recovery for this pattern is 13%, Field Water Cut is 91% and Field Oil Production Rate is 367 STB/day.
Figure 6: 3D View of 7-Spot Inverted at 91% FWCT.
9-SPOT
This pattern resembles 5 spot with an additional 4 wells, each between every two corner wells.it is the second most common pattern used in water flooding having good sweep efficiency.
The Oil Recovery for this pattern is 2%, Field Water Cut is 12% and Filed Oil Production Rate is 561 STB/day.
Figure 7: 3D View of 9 Spot at 12% FWCT.
9 SPOT INVERTED
3 production wells have been placed at every 4 sides of reservoir and 1 injector is placed at the center of reservoir.
The Oil Recovery for this pattern is 21%, Field Water Cut is 85% and Field Oil Production Rate is 114 STB/day.
Figure 8: 3D View of 9-Spot Inverted at 17% FWCT.
DIRECT LINE DRIVE
The producers and injectors are opposite to each other in alternative order. This drive depends upon the distance between two producers and the distance between the injector the opposing producer
The Oil Recovery for this pattern is 15%, Field Water Cut is 89% and Field Oil Production Rate is 199 STB/day.
Figure 9: 3D View of Direct Line Drive At 10% FWCT.
STAGGERED LINE DRIVE
Staggered line drive similar to the direct line but the producers and injectors are not opposite to one another. The line of injectors and producers oppose each other but the number of producers is greater than injectors.
The Oil Recovery for this pattern is 23%, Field Water Cut is 89% and Field Oil Production Rate is 158 STB/day.
Figure 10: 3D View of Staggered Line Drive At 18% FWCT.
Reservoir Description
General Reservoir Properties
NX,NY,NZ 19,19,10
Reservoir Area 380ft2
Average Permeability 195md
Connate water Saturation 0.2
End Point Saturation Oil 0.57
End Point Saturation water 0.25
nO 3.2
nW 1.5
VDP 0.8
Porosity 0.3
Density of oil 45 lb/ft3
Density of Water 63 lb/ft3
Viscosity of Oil 3.3 Cp
Viscosity of Water 0.3 Cp
Datum depth 8430
Datum Pressure 3665
Water Oil Contact 15000 ft
Wellbore diameter 0.67 ft
Oil Production limit 620 bbls/day
Water Injection limit 790 bbls/day
Table 1 shows the Reservoir characteristics of our Model.
Model Calibration
Simulation model has been calibrated with the original reservoir production data using Eclipse100, Original reservoir production data is retrieved from the Field A reservoir [Espinel], history matching is done by calibrating reservoir fluid, rock, production and injection data. Model is calibrated on block centered grid system by using grids (NX=19, NY=19, NZ=10) on 1/8th of 5Spot pattern.
Model ran for 500 days and oil production rate is matched (Figure1).
Numerical Dispersion
Nx, Ny Dx Time (Sec) Recovery Factor
5 76 0.23 16.64
10 38 0.38 17.02
19 20 1.42 17.24
38 10 10.76 17.43
76 5 83 17.54
The area of the reservoir is 380 square ft. that has been divided in to different number of cells i.e. 5, 10, 19, 38, and 76. As number of cells increased, the size of each cell decreased ultimately increase in simulation time for running each file has been seen.
The time taken by the model having number of cells 19 has taken 1.42 seconds to run and we get 17.24% recovery on the other hand the model having number of cells 38 has taken 10.7 seconds to run that is almost 7 times greater time as compared to the above one but only 0.18% increase in recovery takes place.
Table 2 shows number of cells, cell sizes, run time and recovery factor.
So we consider the model having Nx=19 as the most reliable one.
CHAPTER 05
RESULTS
Sensitivity Analysis
Sensitivity Analysis of 5-Spot and Inverted 5-Spot
The Recovery in normal 5-spot i
s 5.2 % whereas water cut is 93% in only 100 days because of excessive amount of water injected by 4 injectors water bypass the oil. The recovery in 5-spot inverted is 23.86% at water cut 90% in 450 days.
Figure 14: Comparative Analysis of Recovery Factor between 5-Spot and 5-Spot Inverted.
Figure 15: Comparative Analysis of Field Water Cut between 5-Spot and 5-S
pot Inverted.
Sensitivity Analysis of 7-Spot and Inverted 7-Spot
The Recovery in normal 7-spot is 3.2 % whereas water cut is 93% in only 75 days because of excessive amount of water injected by 6 injector wells water bypass the oil. The recovery in 7-spot inverted is 20.6% at water cut 88% in 375 days.
Figure 16: Comparative Analysis of Recovery Factor between 7-Spot and 7-Spot Inverted.
Figure 17: Comparative Analysis of Field Water Cut between 7-Spot and 7-Spot Inverted.
Sensitivity Analysis of 9-Spot and Inverted 9-Spot
For 9-Spot, the FOE (recovery factor) is resulted to be very low, about 2.36% because the number of Injection wells are much greater compared to only one production well and the total area of the reservoir is also small for a 9-spot pattern. Due to this reason, water could bypass the remaining oil and starts to produce very fast, the simulation stops at 95% in just 100 days.
While Inverted 9-spot seems to be more efficient as it shows a recovery of 21.3% in the same reservoir and one Injection well is sweeping the oil more efficiently. The simulation stops when field water cut reaches at 85% in 375 days.
Figure 18: Comparative Analysis of Recovery Factor between 9-Spot and 9-Spot Inverted.
Figure 19: Comparative Analysis of Field Water Cut between 9-Spot and 9-Spot Inverted.
Sensitivity Analysis of Direct Line Drive and Staggered Line Drive
The Recovery in direct line drive is 15.6 % whereas water cut is 90% in only 125 days. The recovery in staggered line drive is 23.7% at water cut 90% in 225 days.
3 Production and 3 injection wells are installed considering the factor of cost in drilling more wells in direct line drive. The recovery is same as for 5 Prod and 5 Injection wells. Therefore the installation of 3 wells is optimum number of wells with ultimate recovery and cost effectiveness.
3 Production and 2 injection wells are installed considering the factor of cost in drilling more wells in staggered line drive, the recovery is almost same for 5 prod and 4 Injection wells, therefore the installation of 3 Prod & 2 Injection wells is optimum number of wells with ultimate recovery and cost effective.
Figure 20: Comparative Analysis of Recovery Factor between Direct Line Drive and Staggered Line Drive.
Figure 21: Comparative Analysis of Field Water Cut between Direct Line Drive and Staggered Line Drive.
Sensitivity Analysis of all above patterns
The Recovery factor of Staggered Line Drive is the same as that of Inverted 5-spot but the in Staggered line the oil is produced in less time i.e. 225 days whereas the same amount of oil recovered in Inverted 5-spot but in 450 days that is double of Staggered line drive.
CHAPTER 06
CONCLUSION AND RECOMMENDATION
Conclusion
Reservoir model has been prepared on simulation software Eclipse100 using reservoir properties. It has been calibrated with the original reservoir field production data through history matching. Numerical dispersion has been practiced on the model by using different cell sizes and an optimized cell size of (NX=19, NY=19, NZ=10) was selected on the basis of simulation efficiency and time.
Total 8 scenarios had been implemented that are 5-spot, inverted 5-spot, 7-spot, inverted 7-spot, 9-spot, inverted 9-spot, direct line drive (3 Production and 3 Injection wells) and staggered line drive (3 Production and 2 Injection wells).
It has been found that best scenario is Staggered Line drive with 3 Production and 2 Injection wells, which gives the highest recovery of hydrocarbons within limited time of production. Recovery factor in this well pattern is 24%.
Recommendations
Following recommendations are suggested for future research:
By this simulation studies we have learned the importance of choosing the number of grid blocks in a simulation model, to obtain maximum accuracy in most optimum running time period.
It is highly recommended to run economic analysis on field project before execution.
Environmental effect of water production should be studied before project implementation.
REFERENCES
Tarek Ahmed, “Reservoir Engineering Handbook”, 1946.
Herman Dykstra & R. L. Parsons, “The prediction of oil recovery by water flood”, Secondary recovery of oil in the U.S., 1950, pp 160-174.
J. S. Aronofsky, “Mobility Ratio-Its Influence on Flood Patterns during Water Encroachment”, Vol. 195, pp 15-24, Magnolia Petroleum Co., Dallas, Texas, Petroleum Transactions, AIME, 1952.
F.F. Craig Jr., “Effects of Reservoir description on performance patterns”, SPE AIME, Pan American Petroleum Corp, October 1970, pp 1239-1245.
Surendra P. Singh, Conoco Inc. and O. Gerald Kiel, “Waterflood Design (Pattern, Rate, and Timing)”, SPE 10024 presented at the international petroleum exhibition and technical symposium held in Beijing china 18-26 march 1982.
G. Paul Willhite, “Water flooding”, SPE Textbook Series Vol. 3, 1986.
Mario Farías, José Zaghloul, Turgay Ertekin, and Robert Watson (PNGE – PSU)Terry Pegula, and William Fustos, “Waterflooding in Gordon Sandstone Formation -Taylorstown Field”, U.S. Department of Energy, April 20, 2003.
O. A. Bibars, H. H. Hanafy, “Waterflood Strategy – Challenges and Innovations”, Egyptian General Petroleum Corporation, H. H. Hanafy, SPE, Khalda Petroleum Co., SPE 88774, presented at 11th Abu Dhabi international Petroleum Exhibition and Conference, U.A.E., 10–13 October 2004.
Ruslan GuIyev, “Simulation Study of Areal Sweep Efficiency vs. a function of Mobility ratio & Aspect ratio for Staggered line drive Water flood Patterns”, Submitted to the Office of Graduate Studies of Texas A&M University, August 2008.
A. Bekhiet, Gian Luca, M.El Awady, “Improving oil Reserves through optimizing water flooding patterns at FARAS Field Qattara depression, Western Desert, Egypt”, This paper was presented at the Offshore Mediterranean Conference and Exhibition in Ravenna, Italy, March 25-27,2009.
Amin Rezapour, “Improved Water flooding Performance Using Model Predictive Control”, Faculty of Mechanical, Maritime and Materials Engineering Delft University of Technology, December 10, 2009.
Arsenevsky Ivan, Belayanushkina Maria, Gareev Rustum, Gladkov Andrey, Kondakov Danilla, Lvov Anton, “Comparative Analysis of Different Techniques for waterflooding efficenicy Assessment”, SPE 162112, Presented at SPE Russian Oil and Gas Exploration & Production Technical conference in Moscow, Russia, 16-18 October 2012.
Babak AminShahidy, Mehdi Foroozanfar, “Comparison Between Gas Injection and Water Flooding, in Aspect of Secondary Recovery in One of Iranian Oil Reservoirs”, Global Journal of Science, Engineering and Technology (ISSN: 2322-2441), November 2013,
pp. 87-92.
Ahmed Gsem Alkreem Abbas, Alaa Awad Elkareem Mohammed, Linda Abdul Hafeez Awad, Mohammed Abdul Monim Ibraheem., “Feasibility Study of Improved Oil Recovery through Wate
r flooding In Sudanese Oil Field (Case Study)”, Sudan University of Science and Technology College of Petroleum Engineering & Technology Petroleum Engineering Department, October 2015.
Mohammad Amirul Islam, A.S.M. Woobaidullah and Badrul Imam., “Streamline Simulation study on Recovery of oil by water Flooding: A real case study on HARIPUR field”, Bangladesh J. Sci. Res. 28 (1), June 2015.
Chananchida Supaprom & Akkhapun Wannakomol., “A Comparison Study of Five Spot and Nine Spot Water Injector Patterns to Enhance Oil Recovery of Maesoon Oil Field by Computer Simulation”, International journal of scientific and technical research in engineering (IJSTRE), Volume 2, Issue 4 April 2017, pp 21-26.
APPENDIX
— BE Petroleum 14 Batch
— Dawood university of engineering and technology
— Base case
— July, 2017
RUNSPEC
TITLE
Water Flooding
— NX NY NZ
DIMENS
19 19 10 /
— Phases
Oil
Water
— Units
Field
WELLDIMS
5 10 5 4 /
— Maximum number of saturation (relative permeability) tables
TABDIMS
2* 500 /
— Unified output files
— To put all output data files in one file
UNIFOUT
— Simulation start date
START
1 JAN 2015 06:00:00 /
NSTACK
100
/
GRID
— Size of each cell in X, Y and Z directions
DX
3610*20 /
DY
3610*20 /
DZ
3610*10 /
–TVDSS of top layer only
— X1 X2 Y1 Y2 Z1 Z2
BOX
1 19 1 19 1 1 /
TOPS
361*8400 /
ENDBOX
— (No data means go back to the whole block, 75 values)
PERMX
–no. of cells in a layer*perm
361*300
361*350
361*200
361*300
361*150
361*300
361*200
361*150
361*0.8
361*0.2
/
COPY
PERMX PERMY /
PERMX PERMZ /
/
MULTIPLY
PERMZ 0.1 /
/
PORO
— M = Nx*Ny*Nz
3610*0.3 /
INIT
PROPS
— Densities in lb/ft3
— Oil Wat Gas
— — — —
DENSITY
63 63 0.01 /
— PVT data for dead oil
— P Bo Vis
— —- —- —–
PVDO
–Pres Bo Oil Viscosity
1000 1.08 3.3
5000 1.06 3.3
9000 1.05 3.3
13000 1.03 3.3
17000 1.01 3.3
/
— PVT data for water
— P Bw Cw Vis Viscosibility
PVTW
3665 1.00 3E-06 0.3 0.0 /
— Rock compressibility
— P Cr
ROCK
3665 4E-06 /
— Water and oil rel perms & capillary pressures
— Sw Krw Kro
SWOF
0.2 0 0.57 0
0.25 0.011048543 0.371792501 0
0.3 0.03125 0.227023544 0
0.35 0.057409916 0.126674999 0
0.4 0.088388348 0.062026728 0
0.45 0.123526471 0.024704434 0
0.5 0.162379763 0.006749675 0
0.55 0.204621888 0.000734492 0
0.6 0.25 0 0
/
SOLUTION
— Initial equilibration conditions
— Datum Pi@datum WOC Pc@WOC
EQUIL
8430 3665 15000 0 /
SUMMARY
— Field average pressure
FPR
— Bottom hole pressure of all wells
WBHP
prod
inj /
— Field Oil Production Rate
FOPR
— Field Water Production Rate
FWPR
— Field Oil Production Total
FOPT
— Field Water Production Total
FWPT
— Oil Recovery
FOE
— FIELD Water cut in PROD
FWCT
prod /
— Create Excel readable Run Summary file (.RSM)
EXCEL
SCHEDULE
RPTRST
BASIC=2 /
— Location of wellhead and pressure gauge
— Well Well Location BHP Pref.
— name group I J datum phase
WELSPECS
Inj G1 10 10 8430 water /
Prod1 G2 1 1 8430 oil /
Prod2 G3 1 19 8430 oil /
Prod3 G4 19 19 8430 oil /
Prod4 G5 19 1 8430 oil /
/
— Completion interval
— Well Location Interval Status Well
— name I J K1 K2 O or S ID
COMPDAT
Inj 10 10 1 10 open 2* 0.67 /
Prod1 1 1 1 10 open 2* 0.67 /
Prod2 1 19 1 10 open 2* 0.67 /
Prod3 19 19 1 10 open 2* 0.67 /
Prod4 19 1 1 10 open 2* 0.67 /
/
— Production control
— Well Status Control Oil Wat Gas Liq Resv BHP
— name mode rate rate rate rate rate limit
WCONPROD
‘Prod1’ ‘OPEN’ ‘RESV’ 4* 620 4* /
‘Prod2’ ‘OPEN’ ‘RESV’ 4* 620 4* /
‘Prod3’ ‘OPEN’ ‘RESV’ 4* 620 4* /
‘Prod4’ ‘OPEN’ ‘RESV’ 4* 620 4* /
/
— Injection control
— Well Fluid Status Control Surf Resv Voidage BHP
— NAME TYPE mode rate rate frac flag limit
WCONINJE
‘Inj’ ‘WATER’ ‘OPEN’ ‘RESV’ 1* 790 4* /
/
TUNING
–Record 1: Time stepping controls
1 180 0.1 0.15 3 0.3 0.3 1.20 /
–Record 2: Time truncation and convergence controls
5* 0.1 0.0001 0.02 0.02 /
–Record 3: Control of Newton and linear iterations
2* 150 1* 100 100 /
WECON
Prod1 2* 0.9 2* WELL YES /
Prod2 2* 0.9 2* WELL YES /
Prod3 2* 0.9 2* WELL YES /
Prod4 2* 0.9 2* WELL YES /
/
DATES
1 ‘JAN’ 2015 07:00:00
/
TSTEP
20*25 /
END