SPE 143570

Smart EOR Screening, Breaching the Gap between Analytical and Numerical Evaluations Moreno, Jaime; Gurpinar, Omer; Liu, Yunlong Schlumberger

Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Asia Pacific Oil and Gas Conference and Exhibition held in Jakarta, Indonesia, 20–22 September 2011. 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 Hydrocarbon’s exploration is reaching global frontiers pressuring for near and long term solutions to increase oil production from the existing assets. Field development drivers are shifting towards maintaining a high production plateau while effectively sweeping the reservoir hydrocarbon to maximize recovery. Enhanced oil recovery offers a substantial alternative to improve recovery of green and mature fields by means of enhancing pore level sweeping in the reservoir. While the principles of EOR schemes are not new; field implementation has been scarce and, as a consequence the physics governing the displacement process have not been completely understood. Highly heterogeneous reservoirs in particular pose a challenge for EOR. Geological features influence the EOR agent advancement and their influence needs to be considered on the early stages of EOR screening. A successful EOR selection requires a detailed understanding of the reservoir governing forces together with the reservoir architecture to maximize reservoir conformance. This paper focuses on these issues by proposing a new methodology to aid the EOR selection, addressing the influence of vertical and lateral heterogeneity at the early stages of the screening and providing guidance to maximize contacted oil in the reservoir by reconciling the reservoir driving forces and geological architecture. A comprehensive analysis of the present and past reservoir conditions is used to determine the EOR best suited for the reservoir; data mining, optimized numerical and analytical simulations are used to further substantiate the decision and help mitigate uncertainties by incorporating present and past EOR experience during the selection process. Introduction Understanding rock architecture and fluid flow governing forces is the fundamental key to uncovering the enhanced oil recovery potential of any given reservoir (including measurement scales); with growing hydrocarbon demand, maturing fields and advances in technology fast, effective evaluation of EOR alternatives can substantially increase asset value and mitigate and control development’s risks. Hydrocarbon displacement in the reservoir is dominated by the balance of capillary, gravity and viscous forces. A successful screening requires a detailed understanding of these forces, particularly in heterogeneous reservoirs where depositional environments can heavily influence the advancement of any displacement agent. Furthermore, quantity and quality of data to model rock and fluid interactions in the presence of the injection agent heavily influences the EOR selection and have the potential to mislead decisions if the quality is not up to the required standards. The ability to comprehensively evaluate and select the correct displacement mechanism to optimize the recovery of oil both at pore and reservoir level is highly coveted in the industry today, as operators focus their attention on more effective field management. To optimize decision process, reservoirs need to be evaluated in lights of agent compatibility as well as maximum reservoir conformance by balanced of the dominating fluid flow forces.

2 SPE 143570

Measurement Scales and Force Balance Locating movable hydrocarbon in the reservoir and analyzing displacement potential in gravity/capillarity dominated flows are key for a successful screening of the best suited enhanced-oil-recovery technique for the current field conditions. This requires a detailed understanding of present, past and future behavior of the reservoir in terms of recovery mechanisms, flow patterns as well as geological setting, aiming to take advantage of the current reservoir forces distribution to augment the EOR displacement efficiency. Displacement efficiency estimation is therefore of paramount importance not only on the technical but also the commercial ranking and selection of any enhanced-oil-recovery technique. Estimation of this displacement efficiency is not without a challenge, and one of the major components of the uncertainty is that of scale (often not properly investigated and in some cases ignored altogether); from what is observed at the core level where viscous forces dominate, to near the injectors where viscous and capillary forces are stronger, to formation and field level where the balance of viscous, gravity and capillary forces determines the real efficiency of the displacement. Core plug and to some extent full core displacement processes are often dominated by viscous forces, resulting in a high capillary number and a more efficient sweep of the pore space, the exposed injection surface area of the core (per unit volume) is orders of magnitude greater than the ones expected in the field; providing a high velocity front which is likely to contact most of the pores. Due to the size of the sample, and the duration of the test, the effect of gravity is negligible. Several authors have studied the effect of force balance (at a core scale) on displacement efficiency and the concept of de-saturation curves is widely accepted in the industry1,2,3,4, where the balance of the viscous and capillary forces is taken into account to determine the pore level sweeping efficiency. As a result, areas with 100% contacted hydrocarbon (with the EOR agent) will have, at a pore level scale, different displacement efficiencies (depending on the dominating force balance), furthermore different displacement processes (i.e. water, gas, solvent injection) follow different de-saturation curves requiring not only a static but dynamic estimation of the pore level sweeping efficiency. Fig. 1 shows a typical example of a de-saturation curve. Figure 1 Example of a de-saturation curve

0

5

10

15

20

25

30

35

40

45

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01

Resi

dual

Non

wet

ting

or W

ettin

g Sa

tura

tion,

%

Nvc

Nonwetting critical (Nvc)c

Wetting critical (Nvc)c

Snwr

Swr

Capillary number relates the ratio of viscous to capi and it is defined as: llary forces,

Equation 1 Capillary Number definition

Where is the fluid viscosity, U flow velocity and is the surface or interfacial tension.

SPE 143570 3

is reservoir heterogeneity which often times is not adequately

mparison between the different scales of measurement6

Displacement Efficiency and Rock Quality Distribution t enhanced-oil-recovery displacement processes is

aboratory and pilot studies maybe used to understand the interaction and compatibility between the EOR

Adding to the complexity of the displacement captured in numerical models. Due, in some occasions to the nature of the process the numerical models were trying to address, and others to the limitations of the tools used to image and describe the reservoir. The first issue is easier to address than the latter5, and it has been the subject of several investigations; however, the underlying heterogeneity needs to be considered in the screening and design so that the selected EOR technique(s) is flexible enough to adapt and mitigate to such unfavorable displacement occurrences. Figure 2 shows a schematic representation of the resolution and depth of investigation of several measurements taken in the field, and it highlights the need of a proper representation of the reservoir heterogeneity

Figure 2 Co

Evaluation of the displacement efficiency under differenparamount for the technical and economical selection of the EOR methodology best suited for the field. Displacement mechanisms, however, are subjected to the interaction of the injection agent with both rock and reservoir fluids, and misinterpretation (such as scale issues) and/or lack of this information may cause bias in the analysis with results which are not fully representative of the reservoir behavior, yielding a less than optimal selection for the development. Linjected agent and the reservoir rock and fluids. Miscibility, solid precipitation, foaming, adsorption, core displacements at reservoir conditions, etc are among the common test run in the laboratory to substantiate the selection of any given EOR agent. However, as discussed before, these only partially address the issue of reservoir displacement efficiency, where scale of measurements becomes key to understand the full potential of the EOR technique. Figure 3 and Figure 4 show the results of immiscible displacements on a set of two different rock quality distributions, a younger formation with shallow marine ridge sequence (coarsening upward distribution); and a deeper formation with a more homogeneous high continuity shallow marine distribution. Down dip gas and water injection displacements were introduced, (left display shows a gas displacement, and the one on the right water) and the effect on fluid breakthrough (BT) times and sweeping efficiency were studied. In addition, a series of displacements were conducted where gas was injected after 1 pore volume (PV) of water injection.

4 SPE 143570

Figure 3 Influence of vertical heterogeneity on the overall displacement efficiency (Gravity dominated flow -The warmer colors correspond to the injectant saturation while the blue corresponds to a higher oil saturation)

0

0

0

0

0

0

5

6500

6550

6600

6650

6700

6750

6800

6850

6900

6916

SSTVD1:500

0.00 0.30Phie -0.10 1.10Vsiltcum-0.10 1.10Vsandcum-0.10 1.10VClcum

N7.5

N7.0

N6.0

N5.0

N4.0

N3.0

N2.0

[ ]

water → ← gas

Figure 4: Influence of vertical heterogeneity on the overall displacement efficiency (Viscous dominated flow - The warmer colors correspond to the injectant saturation while the blue corresponds to higher oil saturation)

0

0

0

0

0

0

5

6500

6550

6600

6650

6700

6750

6800

6850

6900

6916

SSTVD1:500

0.00 0.30Phie -0.10 1.10Vsiltcum-0.10 1.10Vsandcum-0.10 1.10VClcum

N7.5

N7.0

N6.0

N5.0

N4.0

N3.0

N2.0

[ ]

← gas water →

The results of the numerical investigation5 show the displacement efficiencies changing with both the property distribution as well as with the forces which are dominating the displacement (gas reached the producer at 0.05PV injected while water did at 0.2PV). Looking at the saturation snapshot taken at EOR agent BT, it is clear that, even at small scales, the EOR agent does not contact the whole hydrocarbon in the formation. In addition, it can be easily shown that the amount contacted is a function of heterogeneity and displacement velocity. Making this an important issue for the screening of any potential EOR displacement where not only reservoir heterogeneity but also force balances are required to estimate the 2-D and 3-D displacement efficiency. Screening There are several drivers which influence the selection of an enhanced-oil-recovery technique, both on the technical sub-surface/Surface and the commercial level. An early understanding and delineation of such challenges greatly aids on the selection process, allowing the technical team to concentrate and acquire data on the critical path of the project in order to mitigate the major risks. Traditionally EOR screening has been mainly influenced by the compatibility of the EOR agent and the reservoir rock and fluid, and a binary approach was used to determine the best suited enhanced-oil-recovery method for a given formation. These evaluations were often augmented by the use of “layer cake” models (analytical and numerical) to estimate recovery. A more detailed analysis was often performed post screening stage, where analogs as well as full field studies were carried out to further substantiate the enhanced oil recovery selection.

SPE 143570 5

This paper proposes a new approach to screening where a guided, data driven process was developed to incorporate all the key reservoir and eor agent drivers at the early stages of the screening; allowing for the selection of the optimum enhanced oil recovery technique for the current conditions and for the spectrum of heterogeneity levels expected in the field. This novel workflow analyzes and evaluates the available field information, and the proper screening technique is selected such that the screening results are reliable and in line with the current field understanding; with hard coded engineering knowledge, decisions are guided through the process to ensure consistency on the results as well as allowing for a faster, repeatable analysis. Figure 5 shows the conceptual schematic of the screening process, where an early evaluation of the current reservoir conditions, is used to understand the location of the remaining hydrocarbon in relation to the rock quality distribution and reservoir driving mechanism. The process recognizes and qualifies the force balance in the reservoir, providing the means of maximizing the contacted hydrocarbon in the field by working together with the existing driving forces of the reservoir to achieve an efficient sweep.

Figure 5. Data driven EOR screening After the hydrocarbon and force distribution of the field have been understood, the existing data validated and the

EOR techniques with most potential identified, present and past successful EOR project information is used to guide the second step of the screening, where reservoirs with rock and fluid properties similar to the ones under study are identified from the published literature data16-37, and used to guide EOR ranking, in conjunction with the reservoir architecture analysis and local displacement efficiency calculations. Figure 6 shows an example of the data mining technique applied for one of the EOR methods were clear trends are observed for different clusters of reservoir and fluid properties. This analysis not only helps substantiate the EOR selection but, in fact allows for an easy identification of field analogues.

Figure 6 Example of Data Mining for EOR Screening

6 SPE 143570

Displacement Efficiency Estimation Once the reservoir forces, hydrocarbon saturation distribution and historical data have been reconciled, the following step is to qualify the potential displacement efficiency under the most likely EOR techniques. Two approaches have been taken in order to quantify the displacement efficiency (data driven), one involving an analytical estimation (traditionally based on a Buckley-Leverett modified approach 7,8,9,10,11,12 where local displacement efficiencies and recovery profiles, are calculated for a given producer/injector flow pattern and a second one which incorporates a higher degree of heterogeneity on a 3-D space, allowing for a different balance of capillary, gravity and viscous forces in order to determine the flow paths of the injected EOR agent onto the reservoir. Results of the analytical optimization are directly incorporated onto the numerical estimation (such as optimum EOR agent concentration, injection ratios and slug sizes). Displacement efficiencies are compared between the analytical and numerical methods to determine the influence of heterogeneity on the contacted and mobilized hydrocarbon. Figure 7 shows the proposed guided workflow for the screening.

Figure 7. EOR screening Workflow*

*Schlumberger TM The challenge described with the scale measurements also relevant when looking at the displacement efficiency estimation, where a representative portion of the reservoir is often used for the estimation of the performance of the EOR agent when contacting the hydrocarbon at reservoir conditions. Analytical methods, alone, do not fully encompass the full heterogeneity of the reservoir; however, when used in conjunction with numerical models, have the potential of helping understanding each of the components of the recovery, from pore level to vertical and volumetric. Furthermore, the results of these methods provide an important guidance for the numerical optimization of the enhanced-oil-recovery techniques. Representative sampling of the reservoir heterogeneity becomes a key feature for the displacement efficiency estimation, driven not only by static reservoir rock features (porosity, permeability, pore throat size distribution) but also by dynamic ones such as pressure and hydrocarbon saturation 5,13,14,15. By its nature, the designed workflow caters for the incorporation of both static and dynamic considerations on the representative reservoir element (RRE) selection, aiming to sample (on a systematic guided fashion) the two end members of the EOR potential (one where reservoir rock is better connected and hydrocarbon saturation is relatively high, and the other one where the connectivity is poorer and the potential recoverable hydrocarbon is lower. A full detailed discussion of the workflow used for the RRE selection will be presented on a follow up paper. Figure 8 shows the results of the displacement efficiency using a combination of analytical and numerical methods for a given representative reservoir element:

SPE 143570 7

Enha

nced

Disp

lacem

ent E

ffici

ency

, %

Enha

nced

Disp

lacem

ent E

ffici

ency

, %

Injected Pore Volume, %Injected Pore Volume, %

Figure 8 Results of Analytical and Numerical Displacement Efficiencies

As mentioned before, completeness and quality of the data drives the selection of the two approaches, however, when the results of the analytical methods were translated onto the numerical ones, and used as guidance on the numerical optimization, a significant improvement on the results quality was observed. The numerical estimation of each EOR method is computationally demanding as each enhanced-oil-recovery technique requires an independent optimization to ensure the results are free of any limitations. Capillary, Gravity and Viscous forces are balanced on a case-by-case basis to ensure reservoir conformance, local displacement efficiencies are also optimized based on the concentration and type of EOR agent injected, and finally an optimum injection schedule is formulated to minimize the use of the EOR agent while maintaining an optimum global recovery. Figure 9 shows the results of a systematic optimization (using the analytical results as guidance) and an independent optimization algorithm; it was clear that the same recovery level may be reached with nearly half the iterations when a systematic approach was used.

Figure 9 Efficiency comparisons between two optimization approaches

Independent Optimization(42 Iterations)

Systematic Optimization (17 Iterations)

Discussion Screening reservoirs for their suitability, both technically and economically, for EOR processes often relies on limited data, particularly at the early stages where the interaction of the EOR agents and the reservoir rock and fluid is not completely understood. Recognizing and understanding these challenges and the impact of uncertainty on the enhanced oil recovery technique selection is critical for the successful selection of the optimum EOR to suit the field under study. While the traditional binary-type screening selection does address some of these issues, it falls short of tackling the dominant force balance of the reservoir and its impact on the hydrocarbon saturation distribution, potentially

8 SPE 143570

limiting the ability of the eor agent to contact the most hydrocarbon within the reservoir. The proposed new approach aids the enhanced-oil-recovery selection incorporating reservoir rock quality distribution as well as the hydrocarbon fluid flow in the selection, with the data-driven model, data quality is assessed and engineering decisions are hardwired within the process such that a technical justifiable decision is reached with the existing information. Furthermore, when dealing with limited and often poor quality data, the approach allows for access to the collective present and past experience on EOR methods not only to validate the EOR selection but also to provide guidance on the subsequent laboratory and pilot design (for the prove of concept phase). A combination of both analyses provides means of a more robust conceptual enhanced-oil-recovery selection. These analyses are substantiated with the use of analytical and/or numerical methods –depending on the type and quality of data available, such that the benefits of the selected enhanced-oil-recovery techniques maybe quantified and compared against each other in terms of incremental sweep as well as efficiency. This paper presents an integrated approach which incorporates all of these considerations in order to provide a data based screening workflow, applicable to a wide range of reservoir types and data quality. Analytical and numerical methods were used on the development of the workflow, on a data-driven basis, demonstrating the validity and relevance of the analytical methods as either estimation of the pore level recovery efficiency or as basis of the numerical optimization. Conclusion We have presented an integrated workflow data driven workflow for EOR screening based on an evaluation of reservoir dominant forces, hydrocarbon and rock quality distribution, as well as present and past EOR experience, empowered by the use of both analytical and numerical methods for displacement efficiency quantification. This allows for dynamic optimization of each EOR method, where different agent properties may be incorporated. We demonstrated their usefulness through a series of automated screening processes. The next stage is to incorporate more physics in the models as well as adding more workflow to be able to explore the best EOR combinations and aid on the field prove of concept design. Acknowledgements The authors wish to thank Schlumberger for permission to publish this paper. References 1. Ramakrishan, T.S and Wasan, D.T: “The Relative Permeability Function for Two-Phase Flow in Porous Media: Effect of Capillary Number,” paper SPE 12693 presented at the 1984 SPE/DOE Symposium on Enhanced Oil Recovery, Tulsa, OK 15-18 April. 2. Stegmeier, G.L.: “Mechanisms of Entrapment and Mobilization of Oil in Porous Media” in “Improved Oil Recovery by Surfactant and Polymer Flooding,” D.O. Shah and Schechter Academic Press, Inc. New York (1977( 3. Taber, J.J., Kirby, J.C. and Shroeder, F.U.: “Studies on the Displacement of Residual Oil: Viscosity and Permeability Effects,” AIChe Symp. Series (1973), 127, 53-56. 4. Melrose, J.C. and Bradner, C.F.: “Role of Capillary Forces in Determining Microscopic Displacement Efficiency,” J. Canad. Pet. Tech. (1974) 54-62. 5. Moreno, J; Flew,S : “EOR: Challenges of Translating Fine Scale Displacement into Full Field Models,” paper SPE 143568-PP presented at the 2011SPE Enhanced Oil Recovery Conference, Kuala Lumpur, Malaysia, 19–21July. 6. Gurpinar et al,: “Has the time come for EOR?” Oilfield Review Magazine, Winter 2010. 7. Pope, G: “The Application of Fractional Flow Theory to Enhanced Oil Recovery”. Paper SPE 7660, January 29 1980. 8. Buckley, S.E. and Leverett, M.C.: “Mechanism of Fluid Displacement in Sands.” Trans. AIME (1942) 146, 107-116 9. Welge, Henry J.: “A Simplified Method for Computing Oil Recovery by Gas or Water Drive” Trans. AIME (1952) 195 91-98 10. Craig, F.F. Jr. “The Reservoir Engineering Aspects of Waterflooding” Monograph Series, Society of Petroleum Engineering, Dallas 1971 11. Patton, J.T., Coats, K.H., and Colegrove, G.T.: “Prediction of Polymer Flood Performance” Society of Petroleum Engineering Journal March 1971, 72-84; Trans AIME, 251 12. Mayberry, D.J.: “The Use of Fractional Flow Theory for Foam Displacement in Presence of Oil” paper SPE 100964 presented at the 2006 SPE Asia Pacific Oil & Gas Conference and Exhibition, Adelaide, Australia 11-13 September 13. Guzman, R.E. et al.: “The Use of Dynamic PseudoFunctions in Reservoir Simulation,” paper presented at the 1994 Intl. Forum on Reservoir Simulation, Muscat, Oman, 10–14 December. 14. Pickup, G.E. and Stephen, K.D.: “Steady-State Scale-up Methods,” paper presented at the 1998 European Conference on the Mathematics of Oil Recovery, Peebles, Scotland, 8–11 September.

SPE 143570 9

15. Kumar, A., Farmer, C.L. and Jerauld, G.R. : “Efficient Upscaling from Cores to Simulation Models”, paper SPE 38744 16. Zhou Wan fu, Wang Xian jun, LI Jian ge, Zhang Li mei: “Application of Colloid Dispersal Gel in Post Polymer Flooding to Improve Recovery”, J. Petroleum Geology & Oilfield Development In Daqing, Vol.2 (2001) 17. Wang De-min, Cheng Jie-cheng, Wu Wen-xiang: “Combining Small Well Spacing with Polymer Flooding to Improve Oil Recovery of Marginal Reservoirs”, J.Special Oil & Gas Reservoirs, Vol.3 (2006) 18. Wang Qimin, JI Baofa, Sui Jun, Guo Wankui, JI Bingyu:”Practice And Knowledge Of Tertiary Recovery Technique In Daqing Oilfield”, J.Petroleum Geology & Oilfield Development In Daqing, Vol.2 (2001) 19. Liu Yikun, Wang Fulin, Sui Xinguang: “Theory research on EOR method of high concentration polymer flooding”, J.Oil Drilling & Production Technology, Vol.6 (2008) 20. Gong Yanfu, Wang Jinmei, Zhang Yanqing: “Polymer Flooding Effect In Medium To Low Permeability Reservoir In Daqing Oilfield”, J.Petroleum Geology & Oilfield Development In Daqing, Vol.2 (2001) 21. Gao Shu-ling, et al: “Improvement of Polymer Flooding in No.13 Xing Pilot Area”, J.Petroleum Geology & Oilfield Development In Daqing,Vol.6(2005) 22. Bi, Yanchang, Su Yanchang & Li Yanxing: “The Use Of Hydraulic Fracturing For Improving Polymer Flooding”,J.Petroleum Geology & Oilfield Deuelopment In Daqing, Vol.3(1999) 23. Zhou Zhi Qi, Duan Qing Hua, Guo Yan Jun, Wang Lu Shan: “Additional Injection Of Chromium Crosslinker Together With Polymer Into Low Pressure Wells During Enlarged Polymer Flooding Tests At Gudao Oil Fields”, J.Oilfield Chemistry, Vol.3(2000) 24. Zhu Huaijiang: “A Study On The Interaction Of Alkali And Partially Hydrolized Polyacrylamide (Hpam) In The Chemical Flooding Processes For Eastern Portion Of Jin 16 Reservoir,Huanxiling Oilfield,Liaohe”, J.Petroleum Expoloration And Development,Vol.5(1992) 25. Li Yongtai And Wen Zhehao: “Analysis Of Effect Of Polymer Flooding In Triassic Extra-Low Permeability Reservoirs With High Salinity In Northern Shanxi Oilfield”, J.Drilling & Production Technology, Vol.3 (2007) 26. Kou Yong-Qiang: “Foam Flood To Enhance Oil Recovery In Block Tuo-11: Laboratory Study And Field Practice”, J.Oilfield Chemistry, Vol.2 (2005) 27. Liu Wei, Qin Xue-Cheng, Tang Jian-Xin, Li Shi, Sun Lei: “Improving Recovery Via Co_2 Huff And Puff In Complex Fault Block Oilfield In Subei Area”, Journal Of Southwest Petroleum University(Science & Technology Edition), Vol.2(2009) 28. Liu Renjing, Liu Huiqing, Li Xiusheng: “Study On The Adaptability Of Nitrogen Foam Flooding For Heavy Oil Reservoir In Shengli Oilfield”, Journal Of Basic Science And Engineering, Vol.1 (2009) 29. Wang Guo-Min, Gao Jiang-Qu, Liu Kong-Zhang, Hu Xin-Ling, Fu Chun-Hua: “Nitrogen Drive Eor Study For Complex Faulted Reservoir”, J.Special Oil & Gas Reservoirs, Vol.3 (2004) 30. Yang Bin, Zhang Mao-Lin, Mei Hai-Yan, Guo Ping, Peng Yu-Lin: “Numerical Simulation Of Immiscible Displacement By Water Alternating Nitrogen In Ma36 Reservoir”, J.Special Oil & Gas Reservoirs, Vol.5(2002) 31. Liu Ping, Zhou Yu, Feng Pei Zhen, Zhao Zhong Xian, Li Jian Rong: “Nitrogen-Gas Flooding In Extremely Low Permeability Reservoir In Block Wei”, Journal Of Jianghan Petroleum Institute,Vol.2(2001) 32. Li Shi-Kui, Zhu Yan, Zhao Yong-Sheng, Lan Yu-Bo: “Evaluation Of Pilot Results Of Alkali-Surfactant-Polymer Flooding In Daqing Oilfield”, Acta Petrolei Sinica, Vol.3 (2005) 33. Sun Chun-Hui, Liu Wei-Dong, Tian Xiao-Chuan: “An Alkaline/Surfactant System For Injection Well Stimulation In Low Permeability And High Temperature Oil Reservoirs”, J. Oilfield Chemistry, Vol.4 (2009) 34. Liu Yigang, Shan Jincheng, Lu Xiangguo, Zhao Lanlan: “Polymer Flooding Agent Selection And Performance Evaluation In Chengbei Oilfield”, J.Offshore Oil, Vol.2 (2009) 35. Li Shu-Xia, Jiang Han-Qiao, Ye Hui-Min, Et Al: “Numerical Simulation Of Surfactant Flooding At Zhen 12 Block”, Journal Of The University Of Petroleum, China, Vol.5 (2003) 36. Wang Zhong-Yuan: “Study On Steam Sweep In Block Qi40 Steam Drive Process”, J. Special Oil & Gas Reservoirs, Vol.4 (2007) 37. Weng Gaofu: “Pilot Research On Oil Displacement By Air-Foam In Shangfa Calcareous Rock Of Baise Oilfield”, J.Oil & Gas Recovery Techinology, Vol.2 (1998)