SPE-78332

SPE 78332 Selection of EOR/IOR Opportunities Based on Machine Learning

Vladimir Alvarado, SPE, PDVSA-Intevep, Aaron Ranson, PDVSA-Intevep, Karen Hernández, PDVSA-Intevep, Eduardo Manrique, SPE, PDVSA-Intevep, Justo Matheus, PDVSA-Intevep, Tamara Liscano, FUNDATEC, Natasha Prosperi, PDVSA-Intevep Copyright 2002, Society of Petroleum Petroleum Engineers Engineers Inc. This paper was prepared for presentation at the SPE 13th European Petroleum Conference held in Aberdeen, Scotland, U.K., 29–31 October 2002. 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, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes 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 where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract The Venezuelan National Oil Company, PDVSA, has dedicated a sustained effort to adapt EOR/IOR technologies to rejuvenate a large number of its mature fields. The first step towards achieving this objective was to select cost-effective technologies suited for conditions of Venezuelan reservoirs. The current strategy for screening EOR/IOR applications is based on the Integrated Field Laboratory philosophy, where a representative pilot area of a number of reservoirs is selected to intensively test EOR/IOR methods, such as WAG injection (water alternating gas) and ASP (alkali polymer surfactant), currently underway. Two problems with this approach are the lack of objective rules to define a reservoir type and the project completion time. In general, the trouble with using expert opinion is that it tends to be too biased by operational experience. It is known that the success of a given EOR/IOR method depends on a large number of variables that characterize a given reservoir. Therefore, the main difficulty for selecting an adequate method is to determine a relationship between reservoir characteristics and the potential of an EOR/IOR method. In this work, data from worldwide field cases have been gathered and data mining was used to extract the experience on those fields. Here, a space reduction method has been used to facilitate the visualization of the needed relationship. Machine learning algorithms have been utilized to draw rules for screening. To illustrate the procedure, several Venezuelan reservoirs have been mapped onto the extracted representation of the international database.

Introduction Primary and secondary recovery methods generally result in recoverable reserves between 40 and 50%. The latter depends

on reservoir complexity and reservoir conditions, field exploitation strategy and is greatly affected by economics. Tertiary recovery or Improved Oil Recovery (IOR) methods are key processes to replace or upgrade reserves, which can be economically recovered, beyond conventional methods. Therefore, the application of IOR methods offers opportunities to replace hydrocarbon reserves that have been produced in addition to those coming from exploration and reservoir appraisal1,2. In this work, we concentrate on screening of EOR processes, rather than IOR, but no real limitation for the method presented here is known at the present time. PDVSA, the Venezuelan National Oil Company, holds a long history of oil and gas production, with all its E & P assets located in Venezuela. This history brings along a large number of mature, near abandonment, reservoirs. PDVSA operates a variety of accumulations, most of them in sandstone formations, with wide spread in API gravity, from bitumen and heavy oils, to volatile oil and condensate reservoirs. Exploitation plans have often yielded low recovery factors, that in average amount to 30% for waterflooding and 40% for gas injection, and lower values for primary recovery in most cases. One of the major difficulties to manage such a portfolio of opportunities relates to numerous reservoirs under dissimilar conditions and the long list of Enhanced Oil Recovery (EOR) technologies available. As expected, screening/ranking of these processes can become a daunting task. Two constraints limit the use traditional evaluation techniques in PDVSA’s case: 1. Maturing reservoirs have short life span, hence hence time is quite limited for the decision-making process. 2. Reservoir characterization is far from complete in a large portion of the portfolio. Although integrated studies are underway, many reservoirs lack enough financial performance to justify information or data gathering. PDVSA-Intevep, PDVSA´s R&D division, has embarked the development and adoption of EOR methods that are suitable for Venezuelan reservoirs. The latter requires techniques for visualization of opportunities with good grasp of the risks involved. This is a consequence of uncertainties, due to incomplete information, a constant in the E & P business. Methods for analysis should be designed to enable

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V. ALVARADO, A. RANSON, K. HERNÁNDEZ, E. MANRIQUE, J. MATHEUS, T. LISCANO, N. PROSPERI

iteration during evaluations, including progressively more details, such that they allow us to refine as the opportunity becomes more attractive and data gathering (reservoir characterization mostly) turns out justifiable. Along with these ideas, PDVSA has developed the concept of the Integrated Field Laboratory (IFL) to facilitate testing field technologies and their deployment in a number of exploitation units. A data mining strategy applied to a collated database of international project results is used here for knowledge extraction on applicability of EOR processes. Statistical analysis of the data yields importance of variables, in terms of how they influence clustering of reservoirs. A small number of these variables, representing average values for each reservoir are used to rank EOR processes and extract rules. It is important to notice that, as mentioned previously, the process of inquiring the database does not end at the first representation of the data, which means that further refinement is necessary, until a decision can be made or information is exhausted from the extraction process. This differs from traditional analysis in the sense that several iterations of the screening/ranking process are not only possible, but also necessary. The paper is organized as follows. After this introduction, a summary of the IFL strategy is summarized. Then, reference to artificial intelligence methods and several EOR screening methodologies is carried out. The proposed methodology is then explained, followed by the results section. Closing remarks and recommendations are provided at the end. This work does not pretend to be comprenhensive, but rather intends to show a first view of a whole strategy thought of for these purposes.

Integrated Field Laboratory The idea behind the IFL philosophy is the speedy evaluation and incorporation of technologies to field operations3. However, finding the best technology for individual reservoirs would represent an endless task. Here comes in the idea of grouping reservoirs by type, i.e. by analyzing together reservoirs with similar characteristics. Applicability criteria for EOR technologies and reservoir types are a motivation for investing in these advanced pilot test areas. One of the weakest aspects in IFL projects, has been the method for extrapolation of learned strategies from a pilot area to a large set of reservoirs. Several EOR technologies are under scrutiny in the IFL projects: Optimized Water flooding 3 (OWF), Alkaline-Surfactant-Polymer (ASP) flooding 4-6, Continuous Steam Floding (CSF) in heavy oil7  and WaterAlternating-Gas (WAG) injection8-10. Fig. 1 illustrates the typical workflow for evaluation in an IFL, for the VLE example. Carvajal et al.3  describe the management approach for technology evaluation in the IFL's as follows: Advanced reservoir characterization, simulation and • visualization. Drainage strategies, combining EOR methods and • well architectures.

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Speedy well construction, with minimum damage and cost. IOR technologies to deal with injectivity and • productivity enhancement. Advanced monitoring technologies. • Production fluid handling technologies. • Risk evaluation and mitigation. • Eight pilot tests have been planned since 1996. As seen in this section, intensive application of technologies in field operations is an important part of the objective in the IFL strategy. However, technology evaluation of that level of detail is only possible in pilot areas. Screening criteria that include the possibility of portafolio analysis is an answer to extrapolation of lessons learned in pilot tests. Alternative approaches, based on artificial intelligence, are now describe to introduce the context of this work. •

Artificial Intelligence Artificial Intelligence (AI), specifically Neural Networks, Fuzzy Logic, and Expert System, have been often proposed and used for supporting E&P operations. Their use varies depending on the specific problem. In the case of neural network and fuzzy logic, they have been proposed for data filtering11 (smoothing), or as modeling tools. All the potential of these information processing systems are used to build nonlinear models for oil production forecast, log interpretation to identify total porosity as well as lithofacies12, or reservoir property related estimations13. In the case of Expert system is mostly used for knowledge representation in the form of IFTHEN rules, where specific Know-How from Experts are used to build schemes that would be automated and used for modeling an Expert reasoning. Some expert system have been developed recently14,15, for different disciplines of E&P, which included drilling areas16,17, well bore simulation18,19, well testing and logging20,21, EOR and fluid property predictions22,23. It is important to mention that combinations of these different tools are also possible, i.e. fuzzy rules are used to increase the capability of the expert system to deal with uncertainties. Also, fuzzy activation functions are used in combination with Neural Network topologies so that some form of regression techniques to adjust the fuzzy set rules are possible. A recent new player in these AI solution suites is Machine Learning (ML) 24. One possible realization of this technique is the combination of Clustering techniques25,26 and rule extraction algorithms27. In this approach, all the data available is used to extract implicit and explicit process or business rules hidden within the data, whether heuristic or not28. In all these cases, AI has showed an excellent performance as well as simplicity in the final solution. With these techniques, the probabilities of success are strongly related to type of information available, the amount/quality and the existing knowledge experience. Therefore, the application prerequisite for these technologies or any deterministic model requires a comprehensive and detail analysis of the problem and the available information. A rule of thumb is that AI techniques are preferred when decisionmaking cases are based on bulk heterogeneous and incomplete

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SELECTION OF EOR/IOR OPPORTUNITIES BASED ON MACHINE LEARNING

information, where deterministic modeling is rather difficult, but each case must be evaluated in case-by-case basis.

EOR Screening During the past 20 years, screening criteria have been employed to evaluate a number of reservoirs for the applicability of different IOR processes in a simple way, before any detailed evaluation is done. This is especially helpful when a large number of reservoirs needs to be analyzed. Screening criteria have evolved through the years and and they are now well established thanks to more field experiences as well as laboratory and numerical simulation studies29-31. Furthermore, several computer programs or analytical model have been developed to select feasible IOR methods and predict their oil recovery performance based on reported screening criteria 32-35. On the other hands, since early 90´s computer technology has improved the application of screening criteria through the use of artificial intelligence techniques to select and design IOR processes and even to perform IOR Project Risk and Economic Analysis 29,36. Typical selection criteria are shown in Table 1. Oil and gas reservoirs represent a complex system with high degree of uncertainty, starting with the definition of the important parameters, finishing with the data availability and then quality. Hence, a first order screening of EOR/IOR methods applicable under particular reservoir conditions is important such that it is possible, in early stages, to establish development scenarios. A primary goal of this work is to propose and develop an AI frame work based on ML, where it is possible to identify, based on a reduced set of characteristics reservoir variables, reservoir clusters, that heuristically will be called reservoir typology. The combination of the reducted space representation with machine learning approach 28  opens a different way of screening EOR methods. It is also possible to visually identify reservoir types, despite the subjective nature of the resulting 2D data representation. In this sense, it is observed that certain types of reservoirs tend to group in specific areas of the maps and these reservoirs have in common the EOR methods that have been applied to them. The proposed approach allows us to carry out fast and clean screening of the EOR methods bases on the “reservoir pseudo typology”. The information used can be handled at different levels of granularity, which means that it guides data capturing, which in turn allows is to request data only in cases where we have foreseen that more information will indeed add more value to the decision-making process.

Results The database used to generate the results contains information from EOR projects carried out around the world, which would allow to compare the results with those from other projects in more than one country or continent. Most of these EOR projects have been completed in the USA and Canada; the remaining projects have been carried out in many other countries from several continents, including Asia, Europe and Latin America. The database includes a list of more than 20 reservoir and fluid variables, although some records are missing information for some of those variables.

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The main sources of information were biannual reports from 1972 through 2000 published by the “Oil & Gas Journal”, articles, books and reports from the SPE, and databases created in PDVSA-INTEVEP. The database contains information from a total of 290 cases, 70% of which correspond to miscible drive and the remaining 30% of the processes use inmiscible drive. Fig. 2 summarizes the statistics of all methods represented in the database. Table 2 shows the range of values for each of the parameters used for the different methods found in the database. The database contains information ranging from extra-heavy oils to condensates, and HP/HT reservoir conditions. Missing conditions are very deep reservoirs, where pressure and temperature exceeds 11.000 psi and 325 oF, respectively, and Tarsands, common in Canadian underground. The first step after database collation and quality control are carried out, is to process the data to generate a knowledge map. Although more than 20 variables were initially considered, to be able to have a large number of records available for the analysis, 6 variables were selected to generate the maps. The selection was based on importance of those variables to form well-defined clusters, but also reduction of redundant information, based on correlation analysis. Fig. 3 shows a projection of the different reservoirs that make up the international database. This type of projections does not intent to represent two axes, as might be interpreted from a 2-D representation, but it instead is a compact representation of a combination of the six variables. After applying cluster algorithm to the projection, six clearly defined clusters can be determined, representing six mixed reservoir typologies, that is, each cluster is made out of different reservoirs to which different EOR methods have been applied. Two examples will be used here to illustrate the use of the information obtained from projections. The way we proceed goes as follows. If a new reservoir, not originally found in the international database, is projected on Fig. 3, and the new reservoir is, for instance, located in cluster 5, this will mean that the newly incorporated reservoir has similar characteristics to those in cluster 5. Analyzing the statistics per method, and per cluster, we can show that most recovery methods in cluster 5 correspond to thermal ones; therefore, if a reservoir is located within cluster 5, experience shows that for this particular typology, thermal methods are frequently used, and perhaps adviced. The method statistics that can be applied to the reservoirs in cluster 5 are shown in Table 3. In addition to the methods statistics that can be applied to each cluster, we have developed a set of rules that allow us to characterize each one of the clusters. The set of rules associated to clusters shown in the projection of Fig.3 are listed in Table 4. It is important to highlight that not all six variables are in principle used to define a rule automatically. This is a consequence of the algorithm employed for this purpose. However, it has been already forseen that rules can be, and will be, complemented by expert opinion, based on observation of the maps.

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V. ALVARADO, A. RANSON, K. HERNÁNDEZ, E. MANRIQUE, J. MATHEUS, T. LISCANO, N. PROSPERI

To find reservoir typologies, representative candidates for specific processes, that is, clusters made out of different reservoirs to which the same EOR method has been applied, each of the six clusters in Fig. 3 are reanalyzed (the same applies to other clusters). This is part of the possible refinement of the data analysis, mentioned before. The latter means that the same methodology and cluster algorithms can be used over the subset of data integrated for each one of the reservoirs that conform cluster 5 or any other cluster (see Fig. 3). For example, looking closely at cluster 5 (Fig. 4), we see once again a new classification (six clusters A, B, C, D, E and F). We also observe that pure typologies, those related to one method only, are better defined. Cluster B and cluster C are clear examples of pure typologies of thermal methods within global cluster 5. Since the new analysis is performed on a reduced set of data of the international database, new results and a different set of rules are ob tained. Figure 5 shows a projection of the international database that includes two Venezuelan reservoirs. For instance, Reservoir A (a relatively shallow light oil reservoir in southern Venezuela- indicated by the dashed Brown Circle) is located in cluster 4. On the other hand, reservoir B (a deep extraheavy oil reservoir in western Venezuela – marked with the dashed Red Circle) is located in cluster 5. For reservoir B, the set of points located within the dotted red circle corresponds to the sensitivity values for some of the process variables in the same reservoir. This leads to the possibility that sensitivity analysis on reservoir history can be explored with the proposed methodology Reanalyzing cluster 4, which contains the Reservoir A (Fig. 5), it can be concluded that the statistics and list of methods that can be applied to this reservoir are those listed in Table 6. If we take a look at reservoir B (Fig. 6 - Dotted Red Circle), we can see that the reservoir belongs to cluster 5 in the general projection map. This means that within a global scope, the reservoir has conditions for thermal methods. However, notice that the original reservoir and its sensitivity values are located on the boundaries of the cluster. However, a zoom of cluster 5 (Fig. 7) shows that reservoir B and its sensitivity values are close to cluster F, but out of the region that defines the cluster. The latter means that by making operational changes such as decreasing the reservoir’s pressure, reservoir B can move into cluster F. The statistics associated with this reservoir are as shown by Table 6 and the set of rules defined for cluster 5 are those in Table 7. The whole procedure applied for the examples discussed here can be used if more variables were considered, as the clustering algorithms are flexible enough to complete this task. Rules derived from the automatic extraction method should , from our point of view, be complemented by using expert opinions, guided in turn by the reduced representation.

Conclusions 1.

Space reduction techniques have been applied successufully to produce bidimensional maps that clearly show reservoir types by using 6 reservoir variables.

2.

3.

4.

5.

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The generated maps allowed us to establish applicability criteria or selection rules, based on international experience on EOR processes. Several Venezuelan reservoirs were mapped, based on their reported average reservoir variables, and sensible conclusions on applicability of EOR processes were drawn from the analysis method proposed here. The outcomes of this work drives further development of the techniques proposed here to refine the screening/ranking criteria based on detailed analysis on the available data. Firmer rules and conclusions can be drawn as the gathered experience, represented in the database, is enlarged. This, however, would require collaboration from oil companies, as the results of the application of EOR methods are not often found in the open litterature.

Acknowledgements We would like to thank PDVSA-Intevep for permission to publish this paper.

References 1. Goodyear, S. G. and Gregory, A. T.: “Risk Assessment and Management in IOR Projects” Paper SPE 28844 presented at the 1994 European Petroleum Conference, London, October 25-27. 2. Thompson, M. A. and Goodyear, S. G.: “Identifying Improved Oil Recovery Potential: A New Systematic Risk Management” Paper SPE 72103 presented at the 2001 Asia Pacific Improved Oil Recovery Conference, Kuala Lumpur, October 8–9. 3. de Carvajal, G., Velasquez, A., Graterol, J., Ramirez, F., and Medina, M.: “Lagomar's Integrated Field Laboratory for Intensive Evaluation of Technologies” Paper SPE 53984 presented at the 1999 SPE Latin American and Caribbean Petroleum Engineering Conference, Caracas, April 21–23. 4. Manrique, E., de Carvajal, G., Anselmi, L., Romero, C., Chacón, L.: "Alkali/Surfactant/Polymer at VLA 6/9/21 Field in Maracaibo Lake: Experimental Results and Pilot Project Design" Paper SPE 59363 presented at the 2000 SPE/DOE Improved Oil Recovery Symposium held in Tulsa, April 3–5. 5. Hernández, C., Chacón, L J., Anselmi, L,, Baldonedo, A,, Qi, J., and Pitts, M. J.: " ASP System Design for an Offshore Application in the La Salina Field, Lake Maracaibo" Paper SPE 69544 presented at the 2001 Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, March 25– 28. 6. Hernández, C., Chacón, L., Anselmi, L., Angulo, R., Manrique, E., Romero, E., de Audemard, N., Carlisle, C.: “Single Well Chemical Tracer Test to Determine ASP Injection Efficiency at Lagomar VLA-6/9/21 Area, C4 Member, Lake Maracaibo, Venezuela” Paper SPE 75122 presented at the 2002 Improved Oil Recovery Symposium, Tulsa, April 13–17. 7. Lopez, E, Rojas, L., Mata, T., Mendoza, H., and Briñez, A.: " Integrated Laboratory Field Application or Thermal Recovery Process" Paper SPE 53983 presented at the 1999 SPE Latin American and Caribbean Petroleum Engineering Conference, Caracas, April 21–23. 8. Manrique, E., Calderón, G., Mayo, L., and Stirpe, M. T.: "WaterAlternating-Gas Flooding in Venezuela: Selection of Candidates Based on Screening Criteria of International Field Experiences" Paper SPE 50645 presented at the 1998 European Petroleum Conference, The Hague, October 20-22. 9. Manrique, E. , Padrón, R., Surguchev, L., De Mena, J., McKenna, K.: "VLE WAG Injection Laboratory Field in Maracaibo Lake"

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SELECTION OF EOR/IOR OPPORTUNITIES BASED ON MACHINE LEARNING

Paper SPE 65128 presented at the 2000 European Petroleum Conference, Paris, October 24-25. 10. Alvarez, C., Manrique, E., Alvarado, V., Samán, A., Surguchev, L., and Eilertsen, T.: "WAG pilot at VLE field and IOR opportunities for mature fields at Lake Maracaibo" Paper SPE 72099 presented at the 2001 Asia Pacific Improved Oil Recovery Conference, Kuala Lumpur, October 8–9. 11. Balch, R. S., Hart, D. M., Weiss, W. W., and Broadhead R. F.: Regional Data Analysis to Better Predict Drilling Success: Brushy Canyon Formation, Delaware Basin, New Mexico” Paper SPE 75145 presented at the 2002 Improved Oil Recovery Symposium, Tulsa, April 13-17. 12. Weiss, W. W., Balch, R. S., and Stubbs B. A.: “How Artificial Intelligence Methods Can Forecast Oil Production” Paper SPE 75143 presented at the 2002 Improved Oil Recovery Symposium, Tulsa, April 13-17. 13. Surguchev, L. and Li, L.: “IOR Evaluation and Applicability Screening Using Artificial Neural Networks” Paper SPE 59308 presented at the 2000 SPE/DOE Improved Oil Recovery Sympsium held in Tulsa, April 3-5. 14. Bergen, J. K. and Hutter, J.E. 1986. The Mudman Service-an artificial intelligence aid for drilling. DrillIng and Production Technoly Symposium PD-vol. 4 (Book No. 100203), The American Society of Mechanical Engineering. 15. Peden, J.M. and Tovar, J.J.: “Sand prediction and exclusion decision support using an expert system” Paper SPE 23165 presented at the 1991 Offshore European Conference, Aberdeen, September 3-6. 16. Einstein, E.E. and Edwars, K.W. 1990. Comparison of an expert system to human experts in well-log analysis and interpretation. SPE Form. Eval. March: 39-45. 17. Allain, O. and Houze, O.P.: “A Practical artificial intelligence application in well testing interpretation” Paper SPE 24287 presented at the 1992 European Petroleum Conference, Stavanger, May 25-27. 18. Hutchin, L.A., Burton, R.K. and Macintosh, D.J.: “An expert system for analyzing well performance” Paper SPE 35705 presented at the 1996 Western Regional Meeting, Anchorage, May 22-24. 19. Alegre,L., Morokooka, C.K. and Rocha, A.F.: “Intelligence Diagnosis of rod pumping problems” Paper SPE 26516 presented at the 1993 Annual Technical Conference, Houston, October 3-6. 20. Patricio, A.R., Rocha, A.F. da and Morooka, C.K.: “Seplant: an expert system for process plant and gas lift well” Paper SPE 28238 presented at the 1994 Petroleum Computer Conference, Dallas, July 31-August 3. 21. Corpoven, M.V.O.: “Real Time Expert System (R.E.T.S) for rod pumping optimization” Paper SPE 34185 presented at the 1995 Petroleum Computer Conference, Houston, June 11-14. 22. Gharbi, R.B. 2000. An Expert System for Selecting and Designing EOR Processes. Accepted for Publication, J.Petrol. Sci.Eng.March 2000. 23. Dharan, M.B., Turek, E.A. and Vogel, J.L.: “The fluid properties measurement expert system” Paper SPE 19134 presented at the 1989 Petroleum Computer Conference, San Antonio, June 2628. “

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24. Mitchell, T. M., “Machine Learning”, Mc Graw Hill, 1997. 25. Ranson, A.; Hernandez, K.; Matheus, J.; Vivas A.: “Monitoring and knowledge extraction in real time multivariable dynamical processes”. ANNIE 2001. 2001. 26. Rujano, R.:  Implementation and evaluation of clustering algorithms using non-deterministic search algorithms to find the optimal number of classes , Thesis PDVSA INTEVEP, 2002. 27. Quinlan, J. R : C4.5 Programs for Machine Learning , Morgan Kaufman Publishers, California 1993. 28. Ranson, A., Hernández, K.Y., Matheus, and Vivas A.A.: “A New Approach to Identifying Operational Conditions in Multivariable Dynamic Processes Using Multidimensional Projection Techniques” Paper SPE 69523 presented at the 2001 Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, March 25–28. 29. Joseph, J., Taber, F., David, M. and Seright R. S.: “EOR Screening Criteria Revisited” Paper SPE/DOE 35385 presented at the 1996 SPE/DOE Symposium on Improved Oil Recovery, Tulsa, April 21-24. 30. Thomas, S., and Farouq, A.: “Field Experience with Chemical Oil Recovery Methods”, Chemical Abstract, Vol. 3, 1995, P:45-3 to 49-3.. 31. Rao, D: Gas Injection EOR a New Meaning in the New Millennium. The Journal of Canadian Petroleum Technology, Feb. 2001, Volume 40, No. 2. 32. Dobitz, J. K. and Prieditis J.: “A Stream Tube Model for the PC” SPE/DOE 27750 presented at the 1994 Symposium on Improved Oil Recovery, Tulsa, April 17-20. 33. K. Thukral and M. Karuppasamy. Hydrocarbon Development Simulation of EOR Applications. Energy, Vol. 16, No. 9, pp. 1207-1212. 1991. 34. T. Okazawa, P. E. Bozac, A. C. Seto, G. R. Howe. Analytical Software for Pool-wide Performance Prediction of EOR Processes. The Jorunal of Canadian Petroleum Technology, April 1995, Vol. 34, No. 4. 35. PRIze: Analytical Model for Evaluating the EOR Potential of Petroleum Reservoir. 1994. 36. Yu, J. P., Zhuang, Z., and Watts, R. J: “Microcomputer Applications in Economic Assessment and Risk Analysis of CO2 Miscible Flooding Process” Paper SPE 19318 presented at the 1989 Eastern Regional Meeting, West Virginia, Oct. 24-27. 37. Basnieva, I. K., Zolotukhin, A. B., Eremin, N. A., and Udovina. E. F.: “Comparative Analysis of Successful Application of EOR in Russia and CIS” Paper SPE 28002 presented at the 1994 University of Tulsa Centennial Petroleum Eng. Symp, Tulsa August 29-31. 38. Chung, T.-H., Carroll, H. B, and Lindsey, R.: “Application of Fuzzy Expert Sistems for EOR Project Risk Analysis” Paper SPE 30741 presented at the 1995 Annual Technical Conference & Exhibition, Dallas, October 22-25. 39. Gharbi, R. B. C.: “An expert system for selecting and designing EOR processes”. Journal of Petroleum and Engineering, 27 (2000) 33-47. 40. Abou-Kassem, J. H. Screening of Oil Reservoirs for selecting Candidates of Polymer Injection. Energy Sources, 21: 5-16, 1999.

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Table 1. Summary of Screening Criteria for Polymer and CO2 Flooding

Parameter

Polymer Flooding > 22 < 100 NC > 50 < 100000 < 5000 2 - 40 < 200 Sandstone preferred > 50 < 9000 NC NC No gas cap and no bottom water drive

Oil Gravity (°API) Oil viscosity(cp) Crude Oil Composition Oil saturation (% PV) Water salinity (ppm) Water hardness (ppm) Mobility ratio Reservoir temperature (°F) Rock type Permeability (mD) Depth (ft) Net thickness Minimum Miscibility Pressure Drive mechanism

29,35,40

CO2 Flooding > 25 < 15 High % C5-C12 fraction > 25 NC NC NC NC Sandstone or carbonate NC > 2500 Wide range < Original pressure No gas cap

Table 2. Range of Values for each Parameter in the entire database.

Parameter

Porosity Temperature Pressure Permeability Gravity Viscosity

Interval

Mean

(%) 5.5-37 17.60 (°F) 60-325 132.89 (psi) 20-10800 2044.77 (mD) 0.2-10500 450.04 (°API) 8.5-55 32.48 (cP) 0.07-5000 72.95

Table 3. Statistics for methods associated with cluster 5.

Method

Air Steam CO2 Immisc. Polymer WAG CO2 Inmisc. Water Flooding N2 Inmisc.

%

41.38 27.59 10.34 8.62 5.17 5.17 1.72

Table 4. Set of Rules defined for International Data Base Projection. Cluster Number RULE 1 2 3

IF POROSITY <= 15.05 && TEMP <= 120.5 && VISC > 3.35 && VISC <= 7.6 Then Cluster 1 IF VISC <= 7.6 && POROSITY <= 15.05 && TEMP > 120.5 && TEMP <= 255 && PRESSURE > 1976 Then Cluster 2 IF POROSITY <= 15.05 &&TEMP <= 120.5 && VISC <= 3.35 Then Cluster 3

4 5 6

IF VISC <= 7.6 && POROSITY > 15.05 &&TEMP <= 145 &&PERM > 375 Then Cluster 4 IF TEMP <= 255 && VISC > 7.6 && PERM > 81.25 && POROSITY > 9.75 Then Cluster 5 IF TEMP > 255 && API > 40.75 Then Cluster 6

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SELECTION OF EOR/IOR OPPORTUNITIES BASED ON MACHINE LEARNING

Table 5. Statistics of recovery methods associated with Cluster 4-C (see Fig. 6).

Method

%

Polymer CO2 Misc. Steam Air Water flooding

33.33 16.67 16.67 16.67 16.67

Table 6. Statistics of methods associated with cluster 5-F of Fig. 6.

Method

CO2 Inmisc. N2 Inmisc. Wag CO2 Inmisc. Polymer

%

40 20 20 20

Table 7. Set of Rules defined for New Analysis Cluster 5 ( Reservoir B). Cluster Number RULE 1

IF IF VISC <= 135 && API > 24.5 && PRESS <= 489.85 Then Cluster 1

2

IF API > 20.05 && VISC > 135  Then Cluster 2

3

IF API <= 20.05 && TEMP <= 107.5 Then Cluster 3

4

IF API <= 20.05 && TEMP > 107.5 &&PERM > 314.75 &&VISC > 95 Then Cluster 4 IF VISC <= 135 && API > 20.05 &&API <= 24.5 Then Cluster 5

5 6

IF API <= 20.05 && TEMP > 107.5 &&PERM <= 314.75 5 Then Cluster 6

7

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V. ALVARADO, A. RANSON, K. HERNÁNDEZ, E. MANRIQUE, J. MATHEUS, T. LISCANO, N. PROSPERI Planning & Conceptualization

Definition & Performance

Preliminary Economy Evaluation (EE)

Main Screening Criteria

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Laboratory evaluation SPE 65128

SPE 50645

Simulation studies and Pilot Design Unsuccessful

New drainage strategies

Project Evaluation and EE

Large field scale application

Project performance and Monitoring

SPE 72099 75259

Successful

Fig. 1. Typical planning for IFLs, illustrated through the VLE example. SPE papers reflect the progress of this particular case. Wag HC Misc. 5% N2 Inmisc. 4% CO2 Inmisc. 5% Wag CO2 Misc. 7% Steam 7%

N2 Misc. 5%

CO2 Misc. 9%

Wag CO2 Inmisc. 1% Wag HC Inmisc. 1% Wag N2 Misc. 1%

Water Flooding 30% Air 10%

Polymer 15%

Water Flooding Polymer Air CO2 Misc. Steam Wag CO2 Misc. CO2 Inmisc. N2 Misc. Wag HC Misc. N2 Inmisc. Wag CO2 Inmisc. Wag HC Inmisc. Wag N2 Misc.

Fig. 2. Distribution of EOR Methods reported in the collated database. Waterflooding abunts in the database, followed by polymer flooding. Scarce data were available for processes such as Nitrogen injection

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Cluster 4 Method

Cluster 5 Method

Air Steam CO2 Immisc. Polymer W AG CO2 Inm isc. Water Flooding N2 Inmisc.

%

41.38 27.59 10.34 8.62 5.17 5.17 1.72

CO2 Immisc. Air W ater Floodin g CO2 Misc. Polymer W AG HC Inmisc. N2 Misc. WAG HC Misc. N2 Inmisc. Steam WAG CO2 Misc.

Cluster 6

%

Method

22.58 12.90 12 .9 0 9.68 9.68 9.68 6.45 6.45 3.23 3.23 3.23

Cluster 6

Method

Method

Water Flooding

CO2 Misc. Polymer N2 Inmisc. Steam WAG HC Misc. CO2 Immisc. WAG CO2 Misc. N2 Misc. WAG N2 Misc.

%

W ater Flooding W AG CO2 Misc. WAG HC Misc. N2 Misc. CO2 Misc. N2 Inmisc. Polymer Air

%

29.17 20.83 18.75 6.25 6.25 6.25 4.17 4.17 2.08 2.08

42.86 21.43 14.29 14.29 7.14

CO2 Inmisc. CO2 Misc. N2 Inmisc. N2 Misc. Polymer Steam Wag CO2 Inmisc. Wag HC Inmisc. Wag CO2 Misc. Wag HC Misc. Air Water Flooding

Cluster 2

Cluster 1

%

N2 Misc. N2 Inmisc. WAG N2 Misc. W ater Flooding WAG HC Misc.

38.46 13.46 13.46 9.62 7.69 7.69 5.77 3.85

Cluster 3 Method

%

W ater Flooding Polymer W AG CO2 Misc . CO2 Misc. N2 Inmisc. WAG HC Misc. Steam

4 8.28 25.29 1 2.6 4 10.34 1.15 1.15 1.15

Fig. 3. International DataBase Projection. Cluster A Method

Cluster B Method

Air Steam

Cluster C Method

%

Air Steam

70 30

%

52.94 47.06

Steam Air Polymer W ater Flooding

%

37.5 25 25 12.5 Cluster E Method

Air Steam CO2 Inmisc. Polymer Water Flooding

CO2 Inmisc. N2 Inmisc. Polymer Steam Air Water Flooding Wag CO2 Inmisc.

Cluster D Method

CO2 Inmisc. Air W ag CO2 Inm isc. Water Flooding Polymer

%

44.44 22.22 11.11 11.11 11.11

%

33.33 22.22 22.22 11.11 11.11 Cluster F Method

CO2 Inmisc. N2 Inmisc. Wag CO2 Inmisc. Polymer

Fig. 4. New Analysis of Cluster 5 of the International Data Base Projection.

%

40 20 20 20

10

V. ALVARADO, A. RANSON, K. HERNÁNDEZ, E. MANRIQUE, J. MATHEUS, T. LISCANO, N. PROSPERI Cluster 4 Method

CO2 Immisc. Air W ater F loodin g CO2 Misc. Polymer W AG HC I nm is c. N2 Misc. WAG HC Misc. N2 Inmisc. Steam W AG CO2 Misc.

Cluster 5 Method

%

Air Steam CO2 Immisc. Polymer W AG CO2 Inm isc . Water Flooding N2 Inmisc.

41.38 27.59 10.34 8.62 5.17 5.17 1.72

Cluster 6

%

22.58 12.90 1 2. 90 9.68 9.68 9. 68 6.45 6.45 3.23 3.23 3.23

Method

Cluster 6

Method

Cluster 1 Method

%

CO2 Misc. Polymer N2 Inmisc. Steam WAG HC Misc. CO2 Immisc. WAG CO2 Misc. N2 Misc. WAG N2 Misc.

29.17 20.83 18.75 6.25 6.25 6.25 4.17 4.17 2.08 2.08

42.86 21.43 14.29 14. 29 7.14

CO2 Inmisc. CO2 Misc. N2 Inmisc. N2 Misc. Polymer Steam Wag CO2 Inmisc. Wag HC Inmisc. Wag CO2 Misc. Wag HC Misc. Air Water Flooding Reservoir A Reservoir B

%

W ater Flooding W AG CO2 Misc. WAG HC Misc. N2 Misc. CO2 Misc. N2 Inmisc. Polymer Air

%

N2 Misc. N2 Inmisc. W AG N2 Misc. W at er Flood ing WAG HC Misc.

Cluster 2

Water Flooding

SPE 78332

38.46 13.46 13.46 9.62 7.69 7.69 5.77 3.85

Cluster 3 Method

%

W at er F lood in g Polymer W AG CO2 Mi sc. CO2 Misc. N2 Inmisc. WAG HC Misc. Steam

4 8. 28 25.29 1 2. 64 10.34 1.15 1.15 1.15

Fig. 5. Venezuelan Reservoir map in the international data base projection, located in clusters 4 and 5 (see dashed circles).

Cluster E Cluster A Method Aire Co2 Inmi Polymer Wag-HCInmi Water Flooding

% 20.0 20.0 20.0 20.0 20.0

Method Co2 Inmi N2_Inmiscible

% 85.7 14.3

Cluster C Method Polymer Water Flooding Steam Co2 Mis Aire

% 33.3 16.7 16.7 16.7 16.7 CO2 Immisc. CO2 Misc. N2 Immisc. N2 Misc. Polymer Steam WAG-HC Immisc. WAG-CO2 Misc.

Cluster B Method Aire Co2 Mis N2_Miscible Wag-HCInmi Wag-HCMisc Water Flooding

% 28.6 14.3 14.3 14.3 14.3 14.3

WAG-HC Misc. air

Cluster D

Water flooding Reservoir A

Method Co2 Mis N2_Miscible Wag-Co2Misc Wag-HCInmi Wag-HCMisc Water Flooding

Fig. 6. New Analysis of Cluster 4 for ReservoirA, clearly indicated by the dashed circle in cluster C.

% 16.7 16.7 16.7 16.7 16.7 16.7

SPE 78332

SELECTION OF EOR/IOR OPPORTUNITIES BASED ON MACHINE LEARNING

Cluster 1 Method

Cluster 2 Method

Air Steam

Cluster 3 Method

%

Air Steam

70 30

%

52.94 47.06

Steam Air Polymer W ater Flooding

11

%

37.5 25 25 12. 5 Cluster 5 Method

Cluster 4 Method

CO2 Inmisc. Air W ag CO2 I nm is c. Water Flooding Polymer

%

Air 44.44 Steam 22.22 CO2 Inmisc. 11.11 Polymer 11.11 W ater Flooding 11.11 CO2 Inmisc. N2 Inmisc. Polymer Steam Air Water Flooding Wag CO2 Inmisc. Series20

%

33.33 22.22 22 .2 2 11.11 11.11 Cluster 6 Method

CO2 Inmisc. N2 Inmisc. Wag CO2 Inmisc. Polymer

%

40 20 20 20

Fig.7. New Analysis of Cluster 5 for Reservoir B, indicated by the dashed brown ellipse. Several points indicate sensitivity analysis.