Field-Scale Simulation of WAG using Pore-Scale Network...

American Journal of Oil and Chemical Technologies; ISSN (online): 2326-6589; ISSN (print): 2326-6570

Volume 2, Issue 3, March 2014

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Field-Scale Simulation of WAG using Pore-Scale Network Modelling

Ramyar Suramairy1, Sebastian Geiger2, Rink van Dijke3, Adnan Al Dhahli4

1. Petroleum Engineering Department, Koya University, Kurdistan Region, Iraq. 2. Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh, UK. 3. Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh, UK.

4. Petroleum Develeopment, Oman.

Corresponding Author Email: [email protected]

Abstract:

In recent years the interest in water alternating gas (WAG) injection as tertiary recovery method has increased. This method has been applied successfully in many fields around the world. The WAG process results in three-phase flow, of which the mechanisms are still not well understood. Therefore; it is important to understand and properly describe the multi-phase flow properties (relative permeability and capillary pressure) which controll the WAG process. In this study we have used pore-scale network modeling to describe the multi-phase flow properties for use in reservoir simulation of WAG injection at the field scale. Network models are being used as alternative for empirical methods to describe the multi-phase flow properties, since the former are physically-based tools which integrate the relevant pore-scale mechanisms while the latter often have little physical basis. A recently developed three-phase flow pore-network model has been used, which integrates the formation and collapse mechanism for oil layers, represents more complexity of the pore structure and incorporates so-called multiple displacement process. In the reservoir simulation we have studied the effect of WAG on recovery factor for different injection scenarios. We additionally studied the effect of different WAG ratios on improving overall WAG performance.

Keyword: Pore-network model, capillary pressure, three-phase, interfacial tension, residual, ternary, ratio, breakthrough, clusters.

1. Introduction In recent years the interest in the Water-alternating –gas (WAG) injection as a recovery method technique has increased. This enhanced oil recovery (EOR) technique has been successfully applied in several oil fields and especially in some Middle East carbonate reservoirs [10]. It has been estimated that more than half of the world’s unrecovered oil is contained in the carbonate rocks in the Middle East. WAG injection tends to improve the oil recovery through contacting the unswept zones, especially the attic or cellar oil by segregation of the gas to the top or the



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accumulation of the water to the bottom of the reservir. WAG may reduces the residual oil in three-phase zones, which arise as a result of injection two fluids (gas and water). In other words, WAG will improve the microscopic displacement [10]. Despite some remarkable progress in the application of WAG, the underlying the three-phase flow mechanisms are still not well understood. Field-scale simulation of WAG must be accompanied by a proper description of the multi-phase flow behaviour which is captured by the relative permeability and capillary pressure functions. It is difficult and expensive to measure the relative permeabilities and capillary pressure for three-phase flow and there are large uncertainties associated with the results at low oil saturations [22]. The measurement of three-phase properties pose a particular challenge because there are two independently varying saturations which results in an infinite number of different displacement paths. Thus, it is impractical to measure relative permeabilities for all three-phase displacements that take place in the reservoir [7]. Hence, empirical expressions have been used to compute the three-phase relative permeabilities and capillary pressures based on more easily available two-phase data [5, 7, 21, 31, 32]. However, those empirical models have no or little physical basis and fail, for example, to capture the oil flow at low saturations resulting in poor prediction of residual oil saturations. An alternative approach has develop physically-based three-phase pore-network models that integrate all the relevant pore-scale mechanism. These modlels are tuned to match the two-phase data in order to predict the three-phase relative permeablities and capillary pressures. This should improve the understanding of three-phase flow and minimize the uncertainty during gas and WAG injection projects. In the network models 2D or 3D arrays of wide pores connected by narrower throats represent the pore structure and will be used as simulation tools to predict the relative permeabilities and capillary pressures [8, 9]. The first representation of multi-phase flow by pore-network models was by Fatt in the 1950’s [13, 14]. Recently, there has been growing interest in using pore-scale network modelling. In the last few years many of these models have been developed [16, 19, 25,26, 27, 29, 34]. The pore-network model that will be used here has been developed to include thermodynamic criteria for formation and collapse of oil layers [35, 36], thus capturing the flow of oil film/layer, which allows accurate prediction of the residual oil saturation and the oil relative permeability at low saturations. This model also offer the possibility of mimicing the complexity of the real pore structure by using inputs of geometrically and topologically equivalent networks. In addition, the model incorporates flow involving disconnected oil clusters that are mobilized through so-called multiple displacements as observed experimentally [30, 31], again contributing to amore accurate prediction of the residual oil saturation [2]. In this paper we have used the pore-network model for Berea sandstone to obtain sets of three-phase relative permeabilties and capillary pressures input for the reservoir model. Then we studied the effect of water alternating gas (WAG) on recovery factor for different injection scenarios in the Brugge field reservoir model as tertiary recovery method in heterogeneous



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reservoirs. We also studied the effect of different WAG ratios on improving overall WAG performance.

2. Methodology

2.1. Network Model Discription The developed model as described by [2] will be used since it is a physically-based simulation tool. This model enables us to obtain the multi-phase flow functions at different rocks wettability. Here we will briefly describe the main features of the model:

1. Thermodynamics criterion for capillary entry pressure for the formation and collapse of the oil clusters has been applied. This will enable the model to accurate prediction of the residual oil at low oil saturation since it succeed to capture the wetting films and flow of the oil that affect the relative permeability. 2. Multiple displacement implementation which allows the accurate modelling of the disconnected phase layers/clusters during high WAG floods. The multiple displacement chain starts with the invaded phase clusters and ends within connected phase to the outlet and the favourable chain during three-phase flow will be found. 3. The model used realistic 3D networks which preserving the topology and pore-space shapes that extracted from reconstructed method and CT images. The network consisting of pore bodies (nodes) connected through narrower throats (bonds) this will enables easier calculation of the intra-pore flow properties.

2.2. Network validation The validation of the networks has been performed and described in details [23]. We briefly review the validation process in this section as has been achieved. The results from both experimentally and the predicted values from the network model which shows excellent agreement. In the water-wet system the three-phase relative permeability of water and gas predicted by the network model and compared to the results determined experimentally by [22] and the conclusion of this is as follow, the gas is the less wetting phase therefore; its relative permeability could be represented as a function of its own saturation. The non-zero gas relative permeability could not be predicted at low gas saturation therefore; the perfect agreement has been observed at high gas saturation figure (1). For the water phase which is the most wetting phase in the system, its relative permeability will be a function of its saturation figure (2). The three-phase oil relative permeability predicted by the network model compared to the experimental data [22]. The predicted oil relative permeabilities are scattered in similar manner as the measured oil relative permeability figure (3). Since the oil in the water-wet system is the intermediate-wetting phase, the oil relative permeability will be a function of both its initial saturation and the saturation profile and this behind the scatter behavior of the experiment data [19, 35].



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Figure 1. Comparison of gas relative permeability by network model with the experiment data of three-phase flow by network model and with experimental [2]



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Figure 2. Comparison of water relative permeability by network model with the experiment data

of three-phase flow by network model and with experimental [2]

Figure 3. Comparison of oil relative permeability by network model with the experiment data of

three-phase by network model and with experimental flow [2]



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2.3. 3D Network Simulation We have run the network simulation to predict the three-phase properties (relative permeabilities and capillary pressures). A realistic 3D pore-network extracted from the reconstructed pore space of Berea sandstone which will be used as inputs data to our model[23]. This network has been altered to produce three networks (A, B and C) table (1)APPENDIX, representing different rock types. The simulation has been carried out for (water-wet):-

Figure 4. Displacement process takes place in network model during the 3D network simulation

C- Water Flooding (Imbibition)

A- Initially the pores of the network are fully saturated with water

During the primary migration (Drainage), the oil invades the pores of low capillary pressure. The water could exist as films/layers.

After the Drainage the wettability of the system (pores) changed by altering the contact angles. The second flood (Imbibition) starts. The water will invade the oil in the pores in which the water existed as films. The oil clusters might be collapsed during that flood

At this stage the gas will be injected into the system. Here there is a possibility of formation of oil layers which separates the water films and gas bulks. This could be created if the gas invaded pores within both oil bulks and water films

D- Gas injection

B- Oil flooding (Primary Drainage)



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3. Measurement of Relative permeabilities and Capillary pressure

3.1. Two-phase flow In order to initialize our reservoir model we need to compute the relative permeability for the two-phase system. We run the network model for two different systems (gas-oil) and (oil-water) systems. In the gas-oil system we injected gas into the network which originally saturated with oil (in the presense of connate water water saturation). This enabled us to obtain the relative permeability of gas (Krg) and the capillary pressure for that system (Pcgo). For the oil-water system, water injected into the pore of the networks which are saturated with oil to the connate water saturation. At this level we determined the water relative permeability (Krw) and the capillary pressure (Pcow) of the oil-water system figure (5).

Figure 5. Two phase relative permeability and capillary pressure curves for network A

0

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0 0.2 0.4 0.6 0.8 1

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e Pe

rmeability

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pillary Pressure

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3.2. Three-phase flow To launch the simulation model for WAG injection the relative permeabilities of three-phase flow should be provided. By simulating our network model, we were able to measure the three-phase relative permeability which presented during the displacement process in each of the networks. Initially the network is fully filled with water (water-wet) rocks figure (4-A). Then oil flooding starts to the network. This representing the primary drainage of the oil from the source rock to the reservoir. The invaded oil will fill the network pores up to the connate water saturation (Swc) and displacing the water figure (4-B). This stage representing the initial reservoir state. After that we have changed the contact angles to represent the wettability alteration in the reservoir during ageing from water-wet to oil-wet. At this stage the network model has been simulated within water flooding (Imbibition) which modelling field production process during water injection figure (4-C). The water injection continued up to the predetermined water saturation (Swi). At the final stage the network simulation within gas injection (tertiary gas injection) has been performed, which representing the production processes in the reservoir within the gas injection figure (4-D). This process repeated for different paths of different Sw values to obtain reasonably set of data which will be able to represent the three-phase relative permeability in the regions within the three-phase saturations in the reservoir figure (6).

Kro Krw



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Figure 6. Three-phase relative permeability curve for network A

4. Reservoir Model & Numerical Simulation

Brugge filed is a complete synthetic field which was built from zero by (TNO). The geological structure of this field consists of stretched half-dome in the east/west direction with a fault of large boundary at the northern edge and an internal fault with 20° angle of modest throw at the northern edge. The field dimensions are about (10x3) km. The reservoir model used the “truth” case which consists approximately of (75x75x2.5) m grid block size with total active grid-blocks of 327,067. This reservoir is under-saturated oil reservoir. Total 30 wells presented 20 producers and 10 injectors figure (1)APPENDIX. Injection scenarios listed in table (2)APPENDIX have been used for this purpose. Three networks have been used which represented types of for the reservoir. These networks have been selected based on their permeability distribution, figure (2)APPENDIX to provide more heterogeneous system. 5. Results and Discussion

5.1. Pore-scale simulation The ternary diagrams in the figures (3, 4, 5)APPENDIX represent the three-phase relative permeability which obtained by 3D network simulation for different saturation (Swi) paths for water-wet system The most common feature for the all the three networks is that the low residual oil could be achieved during gas injection. In the water-wet system high residual oil was observed during the water injection than for gas injection figures (3, 4, 5) APPENDIX. This related to the fact that during the water injection the oil layers might collapse and since in water-wet system oil is the intermediate wetting phase. Therefore; the collapsed oil layers will be

Krg



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surrounded by water wetting films leading to disperse the oil to smaller clusters which reduce the connectivity between the oil clusters and more broken oil clusters trapped. Hence this will result in high residual oil. During the gas injection more oil will be displaced and this attributed to the oil phase which present as a layer phase sandwiched between water films and gas bulks. This will give the possibility of formation of the oil clusters. However; the gas could displace oil when invading pores fill with water films and oil bulks figure (4-D). As water saturation increase less oil will be displaced by gas. This gives an indication that less oil clusters connected to the outlets. The gas then starts to displace water as it connected to the outlet while the oil at this point trapped by water. At the last two saturation paths in network (A), gas displaced directly water since oil phase disconnected and only water connected to outlets. In network (C) it has been observed that gas displacing oil and water alternatively figures (3, 4, 5) APPENDIX. 5.2. Field-Scale WAG simualtion We used the Brugge field reservoir model and the relative permeabilities generated by simulating the networks to run the model for (water-wet) system as stated in table (2)APPENDIX. We investigated the effect of WAG injection on enhancing the recovery by applying the scenarios listed in table (4) APPENDIX. For the water-wet system, the lowest recovery found during injecting only gas figure (7). This related to the early gas breakthrough takes place in the reservoir figure (8). It has been observed that most of the injected gas displacing oil in the upper layers as a result of the gravity segregation before starting to escape and being produced which reducing the recovery figure (8). Implementing WAG injection showed improvement by (3%) in recovery comparing to that one achieved by only water injection at one hydrocarbon pore volume injection figure (7). In figure (8) the difference between red and black lines represents the additional oil recovered by WAG. Recovery during water injection in WAG is higher than for the same period during only water injection and this attributed to the effect of the gas being used during WAG which gives the raise for more oil to be displaced. After some periods, no significant increase observed in WAG recovery comparing to water injection recovery. This related to high mobility of gas and its low density which causes the gas to move to the top of the reservoir. Therefore; gas breakthrough and will be produced, therefore; no significant increase in recovery can be seen. And this due to the fact that gas improving the sweep efficiency if it could be trapped whereas; at this point it escapes leaving residual oil. Therefore; the WAG cycles starting with water flooding are more efficient than those starting with gas flood figure (6)APPENDIX.



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Figure 7. Oil recovery for water-wet system using WAG scenario 7 stated in table (4)

Figure 8. Gas effect on recovery during WAG injection

5.3. Effect of WAG Ratios The effect of WAG ratios on the overall performance has been investigated during water-wet system. We run the model with five WAG ratios (1:1, 1:2, 2:1, and 1:3, 3:1). The 1:1 WAG ratio showed high recovery obtained by different ratios. WAG ratios with high water rate (3:1, 2:1)



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overlapping each other and showed difference in recovery of about (10%) higher than (1:2, 1:3) which are producing more gas figure (9).

Figure 9. Oil recovery for different WAG ratios

6. Conclusion

We simulate WAG injection at field-scale to predict oil recovery by using pore-scale network model which represent physically-based simulation tool. The developed network model [2] being used to simulate three-phase relative permeability used to simulate WAG scenarios with the presence of uncertainty in relative permeabilities and geological realisation of the Brugge field. We used three networks which were derived from pore space reconstructed methods of Berea sandstone. Three-phase relative permeabilities and capillary pressure obtained by simulating the pore-scale networks for water-wet system. The results showed that high residual oil obtained during water flooding than during gas injection. This could be attributed to the snap-off of oil in the pores by the surrounding water and which leads to break the oil layers into small clusters and trapped by water. While during gas injection the formation of the oil layers gives the possibility for more clusters to be connected to the outlets of the pores and hence reducing the residual oil. We then used the outputs (relative permeability and capillary pressure) to run the WAG simulation at reservoir scale. The simulation results showed that lowest recovery achieved by injecting only gas due to high gas mobility and lower density which moves to top of the reservoir and breakthrough. The recovery could be improved by (3.5%) using WAG injection. WAG

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scenarios started with water as first flood were more efficient. Many WAG ratios have been applied and the (1:1) ratio showed significant potential for increasing the recovery factor. 7. References:

[1] Al-Dhahli, A., Geiger, S., and van Dijke, M. I. J. (2011). "Three-Phase Pore-Network Modelling for Mixed-Wet Carbonate Reservoirs." SPE Reservoir Characterisation and Simulation Conference and Exhibition, Society of Petroleum Engineers, Abu Dhabi, UAE. [2] Al-Dhahli, A., Geiger, S., and van Dijke, M. I. J. (2012). "Accurate Modelling of Pore-scale Film and Layer Flow for Three-phase EOR in Carbonate Rocks with Arbitrary Wettability." SPE Improved Oil Recovery Symposium, Society of Petroleum Engineers, Tulsa, Oklahoma, USA. [3] Al-Ghanim, W., R. Gharbi, and M.K. Algharaib, Designing a Simultaneous Water Alternating Gas Process for Optimizing Oil Recovery, in EUROPEC/EAGE Conference and Exhibition. 2009, Society of Petroleum Engineers: Amsterdam, The Netherlands.SPE 120375-MS. [4] Andrews, J., M. Hettema, and T. Nesse, Injection Wells: A Case Study From the Statfjord Field, in SPE Annual Technical Conference and Exhibition. 2004: Houston, Texas.SPE 90949. [5] Baker, L .E. (1993). Three-Phase Relative Permeability of Water-Wet, Intermediate-Wet, and Oil-Wet Sandstone. Seventh European Improved Oil Recovery Symposium, Moscow, Russia. [6] Baojun, F., D. Xingjia, and Y. Cai, Pilot Test of Water Alternating Gas Injection in Heterogeneous Thick Reservoir of Positive Rhythm Sedimentation of Daqing Oil Field. SPE Advanced Technology Series, 1997. 5(1). [7] Blunt, M. J. (2000). An Empirical Model for Three-Phase Relative Permeability. Soc. Pet. Eng. J., 5 (4), 435-445. [8] Blunt, M. J. (2001). "Flow in porous media - pore-network models and multiphase flow." Current Opinion in Colloid & Interface Science, 6(3), 197-207. [9] Blunt, M. J., Jackson, M. D., Piri, M., and Valvatne, P. H. (2002). "Detailed physics, predictive capabilities and macroscopic consequences for pore-network models of multiphase flow." Advances in Water Resources, 25(8-12), 1069-1089. [10] Christensen, J. R., Stenby, E. H., and Skauge, A. (2001). "Review of WAG Field Experience." SPE Reservoir Evaluation & Engineering (04). [12] Fatt I. The network model of porous media I. Capillary pressure characteristics. Trans AIME 1956;207:144_159. [13] Fatt I. The network model of porous media II. Dynamic properties of a single size tube network. Trans AIME 1956;207:160_163. [14] Fatt I. The network model of porous media III. Dynamic properties of networks with tube radius distribution. Trans AIME1956;207:164_181. [15] Fenwick, D. H., and Blunt, M. J. (1998a). "Network Modeling of Three-Phase Flow in Porous Media." SPE Journal, 3(1), 86-96. [16] Fenwick, D. H., and Blunt, M. J. (1998b). "Three-dimensional modeling of three phase imbibition and drainage." Advances in Water Resources, 21(2), 121-143.



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[17] Forrest, J.K., et al., Samarang Field - Seismic To Simulation Redevelopment Evaluation Brings New Life to an Old Oilfield, Offshore Sabah, Malaysia, in International Petroleum Technology Conference. 2009: Doha, Qatar.SPE 13162 [18] Gorell, S.B., Modeling the Effects of Trapping and Water Alternate Gas (WAG) Injection on Tertiary Miscible Displacements, in SPE Enhanced Oil Recovery Symposium. 1988: Tulsa, Oklahoma.SPE 17340 [19] Hui, M.-H., and Blunt, M. J. (2000a). "Pore-Scale Modeling of Three-Phase Flow and the Effects of Wettability." SPE/DOE Improved Oil Recovery Symposium, Copyright 2000, Society of Petroleum Engineers Inc., Tulsa, Oklahoma. [20] Hui, M. H., and Blunt, M. J. (2000b). "Effects of wettability on three-phase flow in porous media." Journal of Physical Chemistry B, 104(16), 3833-3845. [21] Juanes, R., & Spiteri, E. (2004). Impact of Relative Permeability Hysteresis on WAG Injection. SPE 89921. [22] Oak, M. J. (1990). "Three-Phase Relative Permeability of Water-Wet Berea." SPE/DOE Enhanced Oil Recovery Symposium, Tulsa, Oklahoma. [23] Øren, P.-E., and Bakke, S. (2003). "Reconstruction of Berea sandstone and pore-scalemodelling of wettability effects." Journal of Petroleum Science and Engineering, 39(3-4), 177-199. [24] Oren, P.-E., Bakke, S., and Arntzen, O. J. (1998). "Extending Predictive Capabilities to Network Models." SPE Journal, 3(4), 324-336. [25] Øren, P. E., Billiotte, J., and Pinczewski, W. V. (1994). "Pore-Scale Network Modelling of Waterflood Residual Oil Recovery by Immiscible Gas Flooding." SPE/DOE Improved Oil Recovery Symposium, 1994 Copyright 1994, Society of Petroleum Engineers, Inc., Tulsa, Oklahoma. [26] Paterson, L., Lee, J.-Y., and Pinczewski, W. V. (1997). "Three-Phase Relative Permeability in Heterogeneous Formations." SPE Annual Technical Conference and Exhibition, 1997 Copyright 1997, Society of Petroleum Engineers, Inc., San Antonio, Texas. [27] Pereira, G. G. (1999). "Numerical pore-scale modeling of three-phase fluid flow:Comparison between simulation and experiment." Physical Review E, 59(4), 4229–4242. [28] Peters, L., Arts, R., Brouwer, G., Geel, C., Cullick, S., Lorentzen, R. J., Chen, Y., Dunlop, N., Vossepoel, F. C., Xu, R., Sarma, P., Alhuthali, A. H. H., and Reynolds, A. (2010). "Results of the Brugge Benchmark Study for Flooding Optimization and History Matching." SPE Reservoir Evaluation & Engineering(3), pp. 391-405. [29] Piri, M., and Blunt, M. J. (2005a). "Three-dimensional mixed-wet random pore-scale network modeling of two- and three-phase flow in porous media. I. Model description." Physical Review E, 71(2), 026301. [30] Sohrabi (2000) 'Pore scale study of water alternating gas (WAG) injection using high-pressure micromodels with different conditions of wettability', SPE, (95594). [31] Sohrabi (2001) 'Visualisation of Oil Recovery by Water Alternating Gas (WAG) Injection Using High Pressure Micromodels - Oil-Wet & Mixed-Wet Systems', SPE, (71494-MS), pp. 1429-1435. [32] Stone, H. L. (1970). Probability Model for Estimation Three- Phase Relative Permeability. Journal of Petroleum Technology, 20, 214-218.



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[33] Stone, H. L. (1973). Estimation of Three-Phase Relative Permeability and Residual Data, Journal of Canadian Petroleum Technology, 12, 53-61. [34] Suicmez, V. S., Piri, M., and Blunt, M. J. (2006). "Pore-Scale Modeling of Three-Phase WAG Injection: Prediction of Relative Permeabilities and Trapping for Different Displacement Cycles." SPE/DOE Symposium on Improved Oil Recovery, Society of Petroleum Engineers, Tulsa, Oklahoma, USA. [35] Van Dijke, M. I. J., M. Piri, J. O. Helland, K. S. Sorbie, M. J. Blunt, and S. M. Skjæveland (2007), Criteria for three-fluid configurations including layers in a pore with nonuniform wettability, Water Resour. Res., 43, W12S05. [36] Van Dijke, M. I. J. (2003) 'Pore-scale modelling of three-phase flow in mixed-wet porous media: multiple displacement chains', Journal of Petroleum Science and Engineering,39(3-4), pp. 201-216.



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APPENDIX:

Table 1. pore networks parameters Parameters Networks

A B C

Number of Nodes 9315 12786 17657 Number of Bonds 16590 21438 28404

Permeability mD 1390 472.678 29.6618

Clay porosity % 0.0014 0.006506 0.2598

Net porosity % 19.5281 16.5346 7.26506 Total porosity % 19.5295 16.5411 7.52488

Formation Factor 28.9778 48.1238 329.337

Table 2. Properties of the wetting system

Wettability system Case Water-wet

Contact angles (degrees)

θow θgo θgw

0-30 0

0-25.3



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Figure1. Brugge field initial reservoir model

Figure 2. Selection of networks based on porosity and permeability distribution for Brugge field (Peters et al. 2010)



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Table 3. Main reservoir simulation parameters

Parameters Values

Pressure 2,466 psi @ 5577 ft depth

Oil water contact (OWC) 5498.5 ft

Free water level 5505 ft

Compressibility 3.5x10-8 psi-1

Producers and production rates 20 producers (5000 rb/d) @ 725 psi

Injectors and injection rates 10 injectors (10000 rb/d) @ 2611 psi

Water cut allowed 90%

Table 4. WAG injection scenario

Number WAG ratio

Scenario First flood

1 1:1 Pure water injection Water

2 1:1 Pure gas injection Gas

3 1:1 21 WAG cycles (1 year water- 1 year gas) Water

4 1:1 21 WAG cycles (1 year gas- 1 year water) Gas

5 1:1 7 WAG cycles (3 years water- 3 years gas) Water

6 1:1 7 WAG cycles (3 years gas- 3 years water) Gas

7 1:1 4 WAG cycles (5 years water- 5 years gas) Water

8 1:1 4 WAG cycles (5 years gas- 5 years water) Gas



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Figure 3. Network (A) Relative permeabilities

Figure 4. Network (B) Relative permeabilities

Figure 5. Network (C) Relative permeabilities

Kro Krw Krg



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Figure 6. Oil recovery for water-wet system using WAG scenarios stated in table (4)

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