A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis
- Martina Siena (Politecnico di Milano) | Alberto Guadagnini (Politecnico di Milano) | Ernesto Della Rossa (eni S.p.A.) | Andrea Lamberti (eni S.p.A.) | Franco Masserano (eni S.p.A.) | Marco Rotondi (eni S.p.A.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- July 2016
- Document Type
- Journal Paper
- 382 - 390
- 2016.Society of Petroleum Engineers
- Bayesian Clustering, Principal Component Analysis, IOR, EOR screening, Enhanced Oil Recovery
- 5 in the last 30 days
- 795 since 2007
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We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesianclustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.
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Abbas, E. and Song, C. L. 2011. Artificial Intelligence Selection With Capability of Editing a New Parameter for EOR Screening Criteria. J. Eng. Sci. & Technol. 6 (5): 628–638.
Al Adasani, A. and Bai, B. 2011. Analysis of EOR Projects and Updated Screening Criteria. J. Pet. Sci. & Eng. 79 (1–2): 10–24. http://dx.doi.org/10.1016/j.petrol.2011.07.005.
Alvarado, V., Ranson, A., Hernandez, K. et al. 2002. Selection of EOR/IOR Opportunities Based on Machine Learning. Presented at the SPE 13th European Petroleum Conference, Aberdeen, 29–31 October. SPE-78332-MS. http://dx.doi.org/10.2118/78332-MS.
Alvarado, V. and Manrique, E. 2010. Enhanced Oil Recovery: Field Planning and Development Strategies. Oxford: Elsevier Inc.
Anikin, I. 2014. Knowledge Representation Model and Decision Support System for Enhanced Oil Recovery Methods. Presented at the International Conference on Intelligent Systems, Data Mining, and Information Technology, Bangkok, 21–22 April.
Babushkina, E. V., Rusakov, V. S., Rusakov, S. V. et al. 2013. Forecasting IOR/EOR Potential Based on Reservoir Parameters. Presented at the 17th European Symposium on Improved Oil Recovery, St. Petersburg, Russia, 16–18 April. Paper P22. http://dx.doi.org/10.3997/2214-4609.20142666.
Daszykowski, M., Kaczmarek, K., Vander Heyden, Y. et al. 2007. Robust Statistics in Data Analysis–A Review Basic Concepts. Chemometrics and Intelligent Laboratory Systems 85 (2): 203–219. http://dx.doi.org/10.1016/j.chemolab.2006.06.016.
Galas, C. M. F., Clements, A., Jaafar, E. et al. 2012. Identification of Enhanced Oil Recovery Potential in Alberta, Phase 2. Final Report for ERCB.
Gharbi, B. C. R. 2000. An Expert System for Selecting and Designing EOR Processes. J. Petrol. Sci. & Eng. 27 (1–2): 33–47. http://dx.doi.org/10.1016/S0920-4105(00)00049-8.
He, J., Song, Z.-Y., Qiu, L. et al. 1998. High-Temperature Polymer Flooding in Thick Reservoir in Shuanghe Oilfield. Presented at the SPE International Oil and Gas Conference and Exhibition, Beijing, 2–6 November. SPE-50933-MS. http://dx.doi.org/10.2118/50933-MS.
Heller, K. and Ghahramani, Z. 2005. Bayesian Hierarchical Clustering. Presented at the ICML ’05, 22nd International Conference on Machine Learning, Bonn, 7–11 August. http://dx.doi.org/10.1145/1102351.1102389.
Jensen, T. B., Little, L. D., Melvin, J. D. et al. 2012. Kuparuk River Unit Field—The First 30 Years. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–10 October. SPE-160127-MS. http://dx.doi.org/10.2118/160127-MS.
Kamari, A., Nikookar, M., Sahranavard, L. et al. 2014. Efficient Screening of Enhanced Oil Recovery Methods and Predictive Economic Analysis. Neural Comput. & Applic. 25 (3–4): 815–824. http://dx.doi.org/10.1007/s00521-014-1553-9.
Koottungal, L. 2008. 2008 Worldwide EOR Survey. Oil & Gas J. 106 (4): 44–59.
Koottungal, L. 2010. 2010 Worldwide EOR Survey. Oil & Gas J. 108 (14): 36–53.
Koottungal, L. 2012. 2012 Worldwide EOR Survey. Oil & Gas J. 110 (15): 47–59.
Koottungal, L. 2014. 2014 Worldwide EOR Survey. Oil & Gas J. 112 (5): 79–91.
Llano, V., Henthorne, L., and Walsh, J. 2013. Water Management for EOR Applications—Sourcing, Treating, Reuse, and Recycle. Presented at the Offshore Technology Conference, Houston, USA, 6–9 May. SPE-24199-MS. http://dx.doi.org/10.4043/24199-MS.
Manrique, E., Ranson, A., Alvarado, V. et al. 2003. Perspectives of CO2 Injection in Venezuela. Presented at the 24th Annual Workshop and Symposium for the IEA Collaborative Project on Enhanced Oil Recovery, Regina, Saskatchewan, Canada, 7–10 September.
Manrique, E., Izadi, M., Kitchen, C. D. et al. 2009. Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration. SPE Res Eval & Eng 12 (4): 551–561. SPE-113269-PA. http://dx.doi.org/10.2118/113269-PA.
McGuire, P. L., Chatham, J. R., Paskvan, F. K. et al. 2005. Low-Salinity Oil Recovery: An Exciting New EOR Opportunity for Alaska’s North Slope. Presented at the SPE Western Regional Meeting, Irvine, California, USA, 30 March–1 April. SPE-93903-MS. http://dx.doi.org/10.2118/93903-MS.
Moreno, J., Gurpinar, O., and Liu, Y. 2014. EOR Advisor System: A Comprehensive Approach to EOR Selection. Presented at the International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. IPTC 17798.
Sheng, G. 2013. Enhanced Oil Recovery Field Case Studies, first edition, 712. Oxford: Elsevier Inc.
Sirinukunwattana, K., Savage, R. S., Bari, M. F. et al. 2013. Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data With Unknown Statistics. PLoS ONE 8 (10): e75748. http://dx.doi.org/10.1371/journal.pone.0075748.
Surguchev, L. M. and Li, L. 2000. IOR Evaluation and Applicability Screening Using Artificial Neural Networks. Presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, USA, 3–5 April. SPE-59308-MS. http://dx.doi.org/10.2118/59308-MS.
Taber, J. J., Martin, F. D., and Seright, R. S. 1997a. EOR Screening Criteria Revisited–Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Project. SPE Res Eng 12 (3): 189–198. SPE-35385-PA. http://dx.doi.org/10.2118/35385-PA.
Taber, J. J., Martin, F. D., and Seright, R. S. 1997b. EOR Screening Criteria Revisited–Part 2: Implications and Impact of Oil Prices, SPE Res Eng 12 (3): 199–206. SPE-39234-PA. http://dx.doi.org/10.2118/39234-PA.
Tang, S., Tian, Lei, Lu, J. et al. 2014. A Novel Low-Tension Foam Flooding for Improving Post-Chemical-Flood in Shuanghe Oilfield. Presented at the SPE Improved Oil Recovery Symposium, Tulsa, USA, 12–16 April. SPE-169074-MS. http://dx.doi.org/10.2118/169074-MS.
Trujillo-Portillo, M. L., Mercado-Sierra, D. P., Maya, G. A. et al. 2010. Selection Methodology for Screening Evaluation of Enhanced-Oil-Recovery Methods. Presented at the Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru, 1–3 December. SPE-139222-MS. http://dx.doi.org/10.2118/139222-MS.
Uribe, J. P., Pinilla, J. F., and Montes, D. C. C. 2010. Innovative Methodology to Revitalize a Heavy-Oil Mature Field by Identifying Opportunities to Apply New Cycles of Steam Injection. Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru, 1–3 December. SPE-138918-MS. http://dx.doi.org/10.2118/138918-MS.
Vega Riveros, G. and Barrios, H. 2011. Steam Injection Experiences in Heavy and Extra-Heavy Oil Fields, Venezuela. Presented at the SPE Heavy Oil Conference and Exhibition, Kuwait City, 12–14 December. SPE-150283-MS. http://dx.doi.org/10.2118/150283-MS.
Yuqiu, L. and Yali, Z. 2009. Reservoir Screening Criteria for Heavy Oil Thermal Recovery in Liaohe Oilfield. China Oil & Gas 2: 31–35.
Zerafat, M. M., Ayatollahi, Sh., Mehranbod, N. et al. 2011. Bayesian Network Analysis as a Tool for Efficient EOR Screening. Presented at the SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 19–21 July. SPE-143282-MS. http://dx.doi.org/10.2118/143282-MS.