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Development of an analytical model for performance prediction of chemical EOR methods

(Photo: stock.adobe.com)

By M. EL-TAYEB, M. ABU EL ELA, A. EL-BANBI, M. H. SAYYOUH*

*Mohamed El-Tayeb, PhD Candidate at University of Wyoming; Mahmoud Abu El Ela, Ahmed El-Banbi, Professor of Petroleum Engineering at the American University in Cairo; Mohamed Helmy Sayyouh, Pro-fessor of Petroleum Engineering at Cairo University. E-Mail: Mahmoud.Abuelela@worley.com

0179-3187/19/12 DOI 10.19225/191207 © 2019 EID Energie Informationsdienst GmbH

AbstractThe main objective of this study is to develop an analytical model that can predict the perfor-mance of chemical enhanced oil recovery me-thods (polymer and surfactant-polymer floo-ding). The developed model uses the fractional flow theory and the areal and vertical sweep models (such as Craig-Geffen-Morse CGM and streamtube models) to predict several perfor-

mance parameters with time, including oil production rate, water cut, and the cumulative production of oil. The model was validated through several case studies under different conditions. The valida-tion step showed good results for the developed model against the results of a commercial si-mulator “Eclipse”. In addition, the model was run with the data of two actual field applica-tions: the polymer flooding project of the Bre-lum field and the surfactant-polymer flooding project of the North BurBank Pilot. The study indicates that the results of the developed model are almost consistent with (a) the actual per-formance of Brelum field; and (b) the original simulation study results of North BurBank Pi-lot project. The results of the model deviate from the results of the simulator and the field measurements with a range of 5 to 12% only. This match demonstrated the ability and the strength of the developed model.

Unlike previous attempts on this topic, this mo-del takes into consideration the effect of reser-voir heterogeneity and the physical and chemi-cal properties of chemical fluids such as adsorp-tion, permeability reduction, and non-Newto-nian effects for different injection patterns. Accordingly, this model can be used as a pre-simulation tool to support the decision-making during the critical technology selection phase.

1. IntroductionRecently, great efforts have been carried out to develop analytical models that can perform screening and predict the perfor-mance of the Enhanced Oil Recovery (EOR) technologies [1]. Several analytical models have been developed to predict the performance of the chemical EOR processes as shown in Table 1. In 1971, Patton developed the first predictive mo-del [2]. This model is simple and can be

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used as a quick estimator of the additional oil recoverable by a linear polymer flood as a function of oil viscosity, effective po-lymer water viscosity, and level of chemi-cal adsorption. Moreover, it is the only model considering the reservoir to be ho-mogenous. Regarding the other models, the only models that can handle the crossflow between the stratified layers are those of Dake (1979) and Paul et al. (1982) [3, 4]. Concerning the models that can deal with different injection patterns, Paul et al. (1982) and Jones et al. (1984) have combined areal sweep correlations with the 2-D cross-section to consider the areal sweep efficiency [4, 5]. In addition, Giordano (1987) developed a simple mo-del based on streamlines and distribution of velocities in the pattern, so his model can consider the areal sweep efficiency [6]. Regarding the type of flow, all of the available models deal with the flow as dif-fuse flow, except those of Dake (1979), Jones et al. (1984), and Mollaei and Dels-had (2011) [3, 5, 7]. The latter models deal with the flow as a segregated flow.It is obvious from the comparison descri-bed above that all the predictive models differ in assumptions made in their for-mulation to model the features relating to the process. However, all of them are based on frontal advance theory. Generally, there are common assumptions in the above-men-tioned models such as; the ignorance re-garding chemical dispersion, the negli-gence of fluids and rock compressibility, the irreversibility and the instantaneity of the chemical adsorption process, and fi-nally the assumption of isothermal flood. In addition, all of these models assume isotropic and homogenous layers within the stratified reservoir, which in turn me-ans that there is no consideration for the areal heterogeneity. It is clear that these assumptions limit the application of those predictive models and give incomplete re-sults for the purpose of economic analy-sis. Consequently, this study will try to relax these assumptions, if possible, by taking into consideration the effect of reservoir heterogeneity and most of the physical and chemical properties of chemical flu-ids in order to consider most of the reser-voir and process features accompanied by the main chemical EOR processes.

2. Concept and structure of the model 2.1 Model assumptions The main assumptions regarding the pro-posed model are: (1) the fluids and rock are considered incompressible, (2) iso-thermal flood exists, (3) gravity and capil-lary forces are negligible, (4) according to the physical and the chemical properties of the chemical, the salinity effect on the chemical is negligible, (5) viscous finge-ring is also negligible, (6) chemical disper-

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Fig. 1- Model Algorithm

Fig. 1 Model algorithm

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sion is not considered, and finally (7) che-mical adsorption is considered as instanta-neous and irreversible.

2.2 Model capabilitiesThe proposed model includes features that are not available in other analytical models. It takes into consideration the ef-fect of reservoir heterogeneity and the physical and chemical properties of che-mical fluids such as adsorption, permeabi-lity reduction, and non-Newtonian effects

for different injection patterns. Moreover, the proposed model is switch-selectable for either chemical or waterflooding. In addition, it has several options allowing the performance calculations to consider either continuous or slug injection and to deal with either constant or variable injec-tivity to predict the performance of the proposed EOR design process. This model tries to address many of the limitations of other predictive models by taking into consideration all the possible

features relating to the process. Relaxing these limitations will take us some steps forward closer to numerical reservoir si-mulation, yet retaining the simplicity of analytical models. The proposed model therefore has its application in conducting low cost screening and optimization stu-dies prior to detailed reservoir simulation.

2.3 Model Algorithm and StructureThe model algorithm is presented in Fig.ure 1. It should be highlighted that the proposed model can predict the perfor-mance of either water or chemical EOR (polymer or surfactant-polymer) floods. Moreover, the program can deal with eit-her line drive or 5-spot pattern. In the case of a 5-spot pattern, the injectivity and the performance calculations are per-formed according to Craig et al (1955) model [8]. Otherwise, the injectivity and the performance calculations are perfor-med according to the frontal advance the-ory.The developed model was programmed using C# programming language. The cal-culation procedures were programmed via several subroutines. Under a specific condition and type of injection scenario, the model can predict the following para-meters over time: oil production rate, wa-ter production rate, surface water-oil ra-tio, surface water cut, cumulative oil pro-duction, cumulative water production, cumulative injected water, water injection rate, cumulative produced chemical, cu-mulative injected chemical, chemical pro-duced rate, time to breakthrough, slug si-ze at given time, and finally, saturation profile at given time.

2.4 Main concepts and theories of the modelThe model uses the fractional flow theory and the areal and vertical sweep models to predict the performance of the chemi-cal EOR methods. The continuity equa-tions for the water and the chemical spe-cies are also considered in the developed model [9]. These equations are solved analytically by using the method of cha-racteristics to obtain the displacement ef-ficiency [9]. The streamtube approach is also utilized for combining the displacement efficiency with areal sweep efficiency [10, 11]. In this approach, the reservoir is divided into either one, four or eight streamtubes. These streamtubes connect the injector to the producer. Therefore, the 1-D linear displacement is then calculated along each stream-tube by applying the fractio-nal flow theory in the given streamtube. To obtain the production of the 3-D mo-del, the results from all streamtubes and layers are combined [10, 11].Polymer solutions may be subject to shear degradation during the flood. In the pro-posed model, the shear rate around the wellbore is assumed constant throughout

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Cumulative O il Production vs. Time- 1st Case Study

O il Production Rate and Water Cut vs. Time- 1st Case Study

Fig. 2- Performance Curves of the 1st Case Study

Fig. 2 Performance curves of the 1st case study

Tab. 1 Comparison of chemical predictive models

Predictive Model

Reservoir Hetero-geneity

Crossflow Effect

Injection Pattern Type

Flow Type Chemical Type

(Patton, 1971) Homogeneous Not Considered Line Drive Diffuse Polymer

(Dake, 1979) Stratified Considered Line Drive SegregatedImmiscible

Displacement(Paul et al., 1982)

Stratified Considered Different Patterns DiffuseSurfactant-Poly-

mer Flooding(Jones et al., 1984)

Stratified Not Considered Different Patterns Segregated Polymer Flooding

(Giordano, 1987)

Stratified Not Considered Different Patterns DiffuseSurfactant-

Polymer Flooding

(Mollaei, 2011) Stratified Not Considered Line Drive Segregated All*Oil FVF: Oil Formation Volume Factor; **Water FVF: Water Formation Volume Factor

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the flood life. Therefore, polymer viscosity will only be function in polymer concent-ration. In the proposed model, the effect of the non-Newtonian polymer rheology is accounted for in the calculation of in-jection rate. To model the behavior of the non-Newtonian rheology, the modified Blake-Kozeny model for the power-law fluids is used [12]. The proposed treat-ment of other chemical features such as adsorption by rock, permeability reduc-tion, and inaccessible pore volumes are considered within the analytical solution of the frontal advance theory to chemical EOR methods [9].The areal heterogeneity is modeled by using the streamtubes con-cept [10, 11]. Every cell can have its own permeability during the tracking of the flood front. The previous models assume a single value of permeability for each layer.

3. Model Validation 3.1 Description of the hypothetical case

studiesFour hypothetical case studies have been considered to verify, validate and apply the developed model and its subroutines. Table 2 presents the data of the four hypo-

thetical case studies. As shown in Table 2, the 1st and the 2nd case studies are poly-mer flood options with the same rock, flu-id, and chemical properties except the ty-pe of injection. The 1st case study repre-sents a continuous injection scenario, and the 2nd case study shows a slug injection scenario with 0.46 PV slug sizes. The 3rd case study represents the data of a 5-spot pattern consisting of four injectors and a producer in the center of a stratified reser-voir that consists of five layers. The de-tailed rock properties of the stratified re-servoir for the 3rd case study is shown in Table 3. The data of the 4th case study is similar to the data of the 3rd case study ex-cept that the 4th case study shows the abi-lity of the model to deal with areal hetero-geneity by considering streamtubes. Four streamtubes were constructed to simulate the flow in 5-spot pattern. In each streamtube 40 cells were constructed with different permeability in each cell. Table 4 shows the matrix of these permeability values for each cell/streamtube.

3.2 Results of the model validationThe results of the developed model for the

proposed four case studies were compared with the corresponding results of the commercial simulator (Eclipse) as shown in Figures 2 to 5, respectively. It is clear from Figures 2 to 5 that the results of the developed model are almost consistent with the results of Eclipse. Figure 5 pre-sents the results of the complex run (4th case study), which includes a 5-spot hete-rogeneous stratified reservoir with five layers. The figure illustrates that the re-sults of the developed model show a good match with the results of Eclipse, when heterogeneity was considered in the run of the developed model. It is shown in Fi-gure 5 that ignoring the areal hetero-geneity and/or the stratification of the layers will cause the results of the propo-sed model to deviate from those of the numerical simulator.

4. Field ApplicationsThe developed program was run with the data of two actual field applications: poly-mer flooding project of the Brelum field and surfactant-polymer flooding project of the North BurBank Pilot [13, 14].The Brelum field is located in Texas, USA. It has an area of about 1.1 km², and it in-volves 8 producing wells and 12 injectors, settled in a staggered line-drive pattern [13]. The polymer flooding project was started in September 1968. No water had been injected into the reservoir before st-arting the polymer project. The rock and fluid properties of the field and the poly-mer solution parameters are presented in Table 5 [13]. The developed program was run using the data of the Brelum field to predict the performance of the polymer flooding project. Figure 6 shows a compa-rison between the actual field perfor-mance and the results of the predictive model. The comparison shows a good match between the actual performance of the reservoir and the predicted results of the proposed model. This good match ve-rifies the reliability of the developed pro-gram. The North BurBank Pilot is located in Osage County, Oklahoma, USA. It has an area of about 0.04 km² [14]. The surfac-tant-polymer flooding project was started in 1975. The rock and fluid properties of the field and the polymer solution para-meters are presented in Table 6. It should be highlighted that the model was run as a stratified reservoir having detailed speci-fication of the 5 layers as shown in Table 6. The developed program successfully predicted the performance of “North Bur-bank Pilot”. Figure 6 shows a good match between the results of the developed pre-dictive model and the simulation results of the original study in 1975.

5. DiscussionIt is clear that the results of the developed model are consistent and close to the re-

Tab. 2 Input data of the case studies

1st and 2nd case study* 3rd and 4th case study**

Type of injection Continuous & slug injection SlugSlug Size (Pore Volume) Continuous Slug Size 0.46* 0.3Injection Rate (STB/D) 200 VariableBottom-hole Injection Pressure (psi) 5000 1280Distance between injectors (ft) 500 320Distance between producer & injector (ft) 1000 320Oil formation volume factor (res bbl/STB) 1.1 1.104Water formation volume factor (res bbl/STB) 1 1.008Oil specific gravity 0.9 0.904Water specific gravity 1 1Oil viscosity (cp) 40 5Water viscosity (cp) 1 0.6Chemical concentration (ppm) 300 900Polymer viscosity (cp) 4 10.9Inaccessible pore volume 0.25 0Residual resistance factor (Dimensionless) 1.1 1.101Porosity (fraction) 0.267 0.3 Absolute permeability (md) 500 **Connate water saturation 0.3 0.5Irreducible water saturation 0.3 0.5Residual oil saturation 0.3 0.25Total thickness (ft) 20 50Dykstra-Parsons coeffective 0 0.6Corey exponent for oil (Dimensionless) 2 2Corey exponent for water, (Dimensionless) 2 2Grain density (gm/cc) 2.65 2.65Power Law-Exponent, (Dimensionless) 0.818 0.6Power Law-Coefficient, (mPa.sn) 10.9 4.774Adsorption (lb/ac.ft) 17.5 19.5* The two case studies have similar properties except for the type of injection. The 1st case study represents continuous injection. However, the 2nd case study has slug injection. The slug injection size is 0.46 PV. ** The 3rd case study represents a 5-spot pattern consisting of four injectors and a producer in the center of a stratified reservoir that consists of five layers. The detailed permeability of each layer in the 3th case study is presented in Table 3. However, in the 4th case study, four streamtubes were constructed to simulate the flow in the 5-spot pattern. In each streamtube 40 cells were const-ructed with its own permeability. Table 4 shows the matrix of these values for each cell/streamtube.

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sults of the simulator and the field measu-rements. The deviation ranges between 5 to 12%. This match demonstrated the abi-lity and the strength of the proposed mo-del to predict the performance of different injection patterns taking into considerati-on the effects of reservoir heterogeneity (either the layering or the spatial variati-on effects) and the physical and chemical properties of the chemical fluids.Compared with other predictive models, the model has the ability to deal with dif-ferent patterns, not only line drive or 5-spot, but also 7 and 9 spots by using the advantage of the streamtube model to deal with any pattern. Moreover, the mo-del has the ability to deal with areal varia-tion for permeability, which is a general assumption in all of the available predicti-ve models.

6. Conclusions1. An analytical model was developed

and programmed to predict the perfor-mance of chemical EOR methods (Po-lymer and Surfactant-Polymer Floo-ding)

2. The developed model was successfully validated with several hypothetical case studies under different conditions. The results of the developed model showed a good match with the results of a commercial simulator “Eclipse”.

3. The model was run with data of two actual field applications: the polymer flooding project of the Brelum field and the surfactant-polymer flooding project of the North BurBank Pilot. The results of the developed model are con-sistent and match considerably well both with (a) the actual measurements and performance of Brelum field; and (b) the original simulation study results of the North BurBank Pilot project.

4. The results of the model deviate from the results of the simulator and the field measurements with a range of 5 to 12% only. This match demonstrated the ability and the strength of the pro-posed model.

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Cumulative O il Production vs. Time- 2nd Case Study

O il Production Rate and Water Cut vs. Time- 2nd Case Study

Fig. 3- Performance Curves of the 2nd Case Study

Fig. 3 Performance curves of the 2nd case study

Tab. 4 Summary of the permeability data of the 4th case study

Cell Permeability for Streamtube 1 (θ=13.13°) (md)

Permeability for Streamtube 2 (θ=12.94°) (md)

Permeability for Streamtube 3 (θ= 12.13°) (md)

Permeability for Streamtube 4 (θ=6.8°) (md)

1 49.3 59.4 390.3 82.6 39.9 24.6 28.2 19.4 32.5 45.1 20.4 101.8 81.4 113.8 36.5 12.12 162.3 32.8 143 86.4 55.7 70.3 30.4 9.1 47.1 85 25.6 96.6 71.7 101.8 67.8 23.53 438.5 48.9 110.4 126.2 63.6 215.5 49.8 5.2 45 51.9 29 59.7 20.2 80.2 108.1 35.34 492.3 78.6 66.4 36.4 67.2 260.4 117.7 1.9 110.3 29.7 27.8 25 12.2 68.1 127.7 47.75 791.3 152.9 26.8 18.8 73.9 156 69.6 1.7 119.7 32 40.2 19.8 10.6 136.4 256.4 150.26 704.2 48.6 41.6 12.3 45.1 244.6 36.1 5.1 70.9 15.6 39.1 28.7 12.9 167.6 172 105.97 752.3 45.9 45.2 10.1 42.9 238.7 39.4 16.3 43.7 10.3 46.2 55.8 19.8 198.7 159.1 193.18 633 49.6 44.1 12.9 75.4 195.6 29 28.7 28.5 6.9 44.3 59.4 31.7 127.8 149 185.99 542.2 88 37.4 14.5 92.8 53 36.8 65.7 49.2 7.9 30.7 72.4 65.8 9.6 131.2 156.210 471.5 63.4 25.2 111.5 123.2 32 24.7 56.6 106 4.9 41.8 39.1 102.4 17.6 87.2 82.8

Tab. 3 Detailed rock properties of the 3th case study

Layer Porosity Permeability (md) Thickness (ft) Swi1 0.3 529.76 10 0.52 0.3 216.65 10 0.53 0.3 132.61 10 0.54 0.3 81.69 10 0.55 0.3 39.38 10 0.5

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References[1] M. Nageh, M. A. Abu El Ela, S. Tayeb, and M.H. Sayy-

ouh: Application of Using Fuzzy Logic as an Artificial Intelligence Technique in the Screening Criteria of the EOR Technologies, Paper No. SPE 175883-MS, 14th

September, 2015.[2] J. T. Patton, K.H. Coats, and G.T. Colegrove: Predic-

tion of Polymer Flood Performance, SPE Journal, March 1971, 72–84.

[3] L. P. Dake: Fundamentals of Reservoir Engineering, Elsevier/North-Holland Inc., New York, 1979.

[4] W. Paul, L. W. Lake, G. A. Pope, and G. B. Young: A Simplified Predictive Model for Micellar-Polymer Floo-ding, Paper No. SPE 10733, regional meeting of So-ciety of Petroleum Engineers, California USA, 24th–26th March, 1982.

[5] R. S. Jones, G. A. Pope, H. J. Ford, and L. W. Lake: A Predictive Model for Water and Polymer Flooding, Pa-per No. SPE 12653, SPE/DOE 4th Symposium on En-hanced Oil Recovery, Tulsa, 15th-18th April, 1984.

[6] R.M. Giordano, Estimating Field-Scale Micellar/Poly-mer Performance, Paper NO. SPE 16731, Society of Petroleum Engineers Annual Technical Conference, Dallas, Texas USA, 27th–30th September, 1987.

[7] A. Mollaei, and M. Delshad: General Isothermal En-hanced Oil Recovery and Waterflood Forecasting Mo-del, Paper No. SPE 143925, 86th Annual Technical Conference and Exhibition of the Society of Petrole-um Engineers, Denver, Colorado, 30th October-2nd No-vember 2011.

[8] F. F. Craig, T. M. Geffen, and R. A. Morse: Oil Recovery Performance of Pattern Gas or Water Injection Opera-tions from Model Tests, Petroleum Transactions, AI-ME, 1955, Vol. 204, 7–15.

[9] G. A Pope: The Application of Fractional Flow Theory to Enhanced Oil Recovery, SPE Journal, 1980, 20, No. 3: 191–205.

[10] R. V. Higgins, and A. J. Leighton: A Computer Method to Calculate Two- Phase Flow in Any Irregularly Boun-ded Porous Medium, Journal of Petroleum Technolo-gy, Vol. 14, Issue 6, June 1962, pp. 679–683.

[11] R. V. Higgins, and A. J. Leighton: Computer Prediction of Water Drive of Oil and Gas Mixtures Through Irre-gularly Bounded Porous Media Three-Phase Flow, Journal of Petroleum Technology, Vol. 14, Issue 9, Sep. 1962, pp. 1048–1054.

[12] G. J. Hirasaki, and G. A. Pope: Analysis of Factors In-fluencing Mobility and Adsorption in the Flow of Poly-mer Solution Through Porous Media, Society of Pet-roleum Engineers Journal, 1974, Vol. 14, No. 4.

[13] R. J. Rowalt: A Case History of Polymer Waterflooding Brelum Field Unit, paper SPE 4671-Ms, 1st January 1973.

[14] J. C. Trantham, H. L. Patterson, and D. F. Boneau: The North Burbank Unit, Tract 97 Surfactant/Polymer Pilot – Operation and Control, Journal of Petroleum Tech-nology, 1975, Vol.30, No.7.

Mohamed EL-Tayeb is currently a PhD Candidate at University of Wyo-ming. He graduated from Cairo Uni-versity (Petroleum Engineering De-partment) in May 2013 and got his master’s degree from the same uni-

versity as well in September 2017. His research and experience in petroleum engineering mostly focuses on enhanced oil recovery (EOR).

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Cumulative O il Production vs. Time- 3rd Case Study

O il Production Rate and Water Cut vs. Time - 3rd Case Study

Fig. 4- Performance Curves of the 3rd Case Study

Fig. 4 Performance curves of the 3rd case study

Tab. 5 Data of Brelum field

Parameter ValueAverage thickness (ft) 10Average porosity 0.293Average permeability (md) 400Dykstra-Parsons coefficient 0.69Irreducible water saturation (Swi) 0.44

Residual oil saturation (Sor) 0.28Oil relative permeability @ Swi 1Water relative permeability @ Sor 0.39Corey exponent for water, Dimensionless 1.8Corey exponent for oil (Dimensionless) 2Oil formation volume factor (res bbl/STB) 1.03Oil viscosity (cp) 10Oil density (Ibm/ft3) 57.5Water density (Ibm/ft3) 64.5Water viscosity (cp) 0.65)Injected polymer concentration (ppm) 400Polymer slug size 0.25 PVPolymer viscosity coefficient, a 0.3125Polymer intrinsic viscosity (dl/gm) 36Adsorption (Ibm/acre.ft) 200Inaccessible pore volume 0.2Residual resistance factor 2.79

Mohamed El-Tayeb

Mohamed EL-Tayeb is currently a PhD Candidate at University of Wyoming. He graduated from Cairo University (Petroleum Engineering Department) in May 2013 and got his master’s degree from the same university as well in September 2017. His research and experience in petroleum engineering mostly focuses on enhanced oil recovery (EOR).

Mahmoud Abu El Ela is a professor of petroleum engineering at Cairo University. He is also Managing Director for the Mining Studies & Research Center (MSRC) at the Faculty of Engineering, Cairo University.

reservoir engineering

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Mahmoud Abu El Ela is a professor of petroleum engineering at Cairo University. He is also Managing Di-rector for the Mining Studies & Re-search Center (MSRC) at the Faculty of Engineering, Cairo University.

Ahmed El-Banbi is a professor of petroleum engineering and chair of the Petroleum Engineering Depart-ment at the American University in Cairo. He previously worked for Cai-ro University. Prior to that, El-Banbi

worked for Schlumberger, where he held a variety of technical and managerial positions in five countries.

Dr. Mohamed Helmy Sayyouh ob-tained his B.Sc. and MS. Degrees in petroleum engineering from Cairo University in 1970 and 1974 res-pectively and Ph.D. from Penn State University, USA in 1979 and beca-

me an Assistant Professor of reservoir engineering.

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Comparison between the results of the predictive model and the actual performance of Brelum field

Original Simulator Program

Comparison between the results of the predictive model and the original simulation study results of the North BurBank Pilot Project

Comparison between the results ofthe predictive model and the actual performance of Brelum field

Original Simulator Program

Comparison between the results of thepredictive model and the original simulation study results of the North BurBank Pilot Project

Fig. 6- Performance Curves of the two fields applications: Brelum field and North BurBank Pilot Project

Fig. 6 Performance curves of the two fields applications: Brelum field and North BurBank pilot project

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Cumulative O il Production vs. Time- 4th Case Study

O il Production Rate and Water Cut vs. Time- 4th Case Study

Fig. 5

Tab. 6 Summary of the input data of North BurBank pilot

Parameter ValueDepth (ft) 3000 Formation temperature (°F) 118 Formation pressure (psi) 915 API gravity (API scale) 38 Oil formation volume factor (res bbl/STB) 1.050 Water formation volume factor (res bbl/STB) 1.000 Oil viscosity (cp) 3.0 Water viscosity (cp) 0.6 Injectivity coefficient (psi/ft) 0.25 Polymer concentration (ppm) 250 Surfactant concentration (ppm) 1500 Surfactant adsorption (lb/ac.ft) 25 Polymer viscosity (cp) 1.2 Polymer adsorption (lb/ac.ft) 15.0 Polymer slug (Pore Volume) 0.3Surfactant slug (Pore Volume) 0.15Residual resistance factor (dimensionless) 4.0Power law exponent (dimensionless) 0.67Power law coefficient (cp (sec)n-1) 7.5

Detailed Specifications of the Reservoir Layers

Layer Porosity (Fraction) Permeability (md) Thickness (ft)Initial Water Saturation (Fraction)

1 0.3 3000 8 0.772 0.29 2000 3 0.753 0.25 558 1 0.684 0.12 5 2 0.455 0.17 25 28 0.53

Ahmed El-Banbi is a professor of petroleum engineering and chair of the Petroleum Engineering Department at the American University in Cairo. He previously worked for Cairo University. Prior to that, El-Banbi worked for Schlumberger, where he held a variety of technical and managerial positions in five countries.

Dr. Mohamed Helmy Sayyouh obtained his B.Sc. and MS. Degrees in petroleum engineering from Cairo University in 1970 and 1974 respectively and Ph.D. from Penn State University, USA in 1979 and became an Assistant Professor of reservoir engineering.

Ahmed El-Banbi is a professor of petroleum engineering and chair of the Petroleum Engineering Department at the American University in Cairo. He previously worked for Cairo University. Prior to that, El-Banbi worked for Schlumberger, where he held a variety of technical and managerial positions in five countries.

Dr. Mohamed Helmy Sayyouh obtained his B.Sc. and MS. Degrees in petroleum engineering from Cairo University in 1970 and 1974 respectively and Ph.D. from Penn State University, USA in 1979 and became an Assistant Professor of reservoir engineering.

Mohamed El-Tayeb

Mohamed EL-Tayeb is currently a PhD Candidate at University of Wyoming. He graduated from Cairo University (Petroleum Engineering Department) in May 2013 and got his master’s degree from the same university as well in September 2017. His research and experience in petroleum engineering mostly focuses on enhanced oil recovery (EOR).

Mahmoud Abu El Ela is a professor of petroleum engineering at Cairo University. He is also Managing Director for the Mining Studies & Research Center (MSRC) at the Faculty of Engineering, Cairo University.