Capacitance Resistance Clustered Model for Mature Peripheral Waterflood Performance Prediction & Optimization
Abstract
Optimizing water injection rate distribution in waterflooding operations is a vital reservoir management aspect since water injection capacities may be constrained due to geographic location and facility limitations. Traditionally, numerical grid-based reservoir simulation is used for waterflood performance evaluation and prediction. However, the reservoir simulation approach can be time-consuming and expensive with the vast amount of wells data in mature fields.
Capacitance Resistance Model (CRM) has been widely used recently as a data-driven physics-based model for rapid evaluation in waterflood projects. Even though CRM has a smaller computation load than numerical reservoir simulation, large mature fields containing hundreds of wells still pose a challenge for model calibration and optimization. In this study, we propose an alternative solution to improve CRM application in large-scale waterfloods that is particularly suitable for peripheral injection configuration. Our approach attempts to reduce CRM problem size by employing a clustering algorithm to automatically group producer wells with an irregular peripheral pattern. The selection of well groups considers well position and high throughput well (key well). We validate our solution through an application in a mature peripheral waterflood field case in South Sumatra. Based on the case study, we obtained up to 18.2 times increase in computation speed due to parameter reduction, with excellent history match accuracy.
Full text article
References
Albertoni, A., & Lake, L. W. (2003). Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods. SPE Reservoir Evaluation & Engineering, 6(01), 6–16. https://doi.org/10.2118/83381-PA
Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. SODA ’07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035. https://dl.acm.org/doi/10.5555/1283383.1283494
Artun, E. (2017). Characterizing interwell connectivity in waterflooded reservoirs using data-driven and reduced-physics models: A comparative study. Neural Computing and Applications, 28(7), 1729–1743. https://doi.org/10.1007/S00521-015-2152-0/METRICS
Balaji, K., Rabiei, M., Suicmez, V., Canbaz, H., Agharzeyva, Z., Tek, S., Bulut, U., & Temizel, C. (2018, June 11). Status of Data-Driven Methods and their Applications in Oil and Gas Industry. SPE Europec Featured at 80th EAGE Conference and Exhibition 2018. https://doi.org/10.2118/190812-MS
Byrd, R. H., Hribar, M. E., & Nocedal, J. (1999). An Interior Point Algorithm for Large-Scale Nonlinear Programming. SIAM Journal on Optimization, 9(4), 877–900. https://doi.org/10.1137/S1052623497325107
Davudov, D., Malkov, A., & Venkatraman, A. (2020, August 30). Integration of Capacitance Resistance Model with Reservoir Simulation. SPE Symposium on Improved Oil Recovery. https://doi.org/10.2118/200332-MS
Ershaghi, I., & Abdassah, D. (1984). A Prediction Technique for Immiscible Processes Using Field Performance Data (includes associated papers 13392, 13793, 15146 and 19506 ). Journal of Petroleum Technology, 36(04), 664–670. https://doi.org/10.2118/10068-PA
Gentil, P. H. (2005). The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients [The University of Texas at Austin]. https://repositories.lib.utexas.edu/handle/2152/81125
Gildin, E., & Lopez, T. J. (2011). Closed-Loop Reservoir Management: Do We Need Complex Models? SPE Digital Energy Conference and Exhibition, 509–519. https://doi.org/10.2118/144336-MS
Guo, Z., Reynolds, A. C., & Zhao, H. (2018). Waterflooding optimization with the INSIM-FT data-driven model. Computational Geosciences, 22(3), 745–761. https://doi.org/10.1007/S10596-018-9723-Y/METRICS
JAMALI, A., & ETTEHADTAVAKKOL, A. (2017). Application of capacitance resistance models to determining interwell connectivity of large-scale mature oil fields. Petroleum Exploration and Development, 44(1), 132–138. https://doi.org/10.1016/S1876-3804(17)30017-4
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis (L. Kaufman & P. J. Rousseeuw, Eds.). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470316801
Kiær, A., Lødøen, O. P., de Bruin, W., Barros, E., & Leeuwenburgh, O. (2020). Evaluation of a data-driven flow network model (flownet) for reservoir prediction and optimization. ECMOR 2020 - 17th European Conference on the Mathematics of Oil Recovery, 2020(1), 1–18. https://doi.org/10.3997/2214-4609.202035099/CITE/REFWORKS
Liu, H. H., Zhang, J., Liang, F., Temizel, C., Basri, M. A., & Mesdour, R. (2021). Incorporation of Physics into Machine Learning for Production Prediction from Unconventional Reservoirs: A Brief Review of the Gray-Box Approach. SPE Reservoir Evaluation & Engineering, 24(04), 847–858. https://doi.org/10.2118/205520-PA
Møyner, O., Krogstad, S., & Lie, K. A. (2015). The Application of Flow Diagnostics for Reservoir Management. SPE Journal, 20(02), 306–323. https://doi.org/10.2118/171557-PA
Nguyen, A. P., Kim, J. S., Lake, L. W., Edgar, T. F., & Haynes, B. (2011). Integrated Capacitance Resistive Model for Reservoir Characterization in Primary and Secondary Recovery. SPE Annual Technical Conference and Exhibition, 5, 4162–4181. https://doi.org/10.2118/147344-MS
Sayarpour, M., Zuluaga, E., Kabir, C. S., & Lake, L. W. (2009). The use of capacitance–resistance models for rapid estimation of waterflood performance and optimization. Journal of Petroleum Science and Engineering, 69(3–4), 227–238. https://doi.org/10.1016/J.PETROL.2009.09.006
Temizel, C., Energy, A., Nabizadeh, M., Kadkhodaei, N., Ranjith, R., Suhag, A., Balaji, K., & Dhannoon, D. (2017, May 9). Data-Driven Optimization of Injection/Production in Waterflood Operations. SPE Intelligent Oil and Gas Symposium 2017. https://doi.org/10.2118/187468-MS
Thakur, G. C. (1991). Waterflood surveillance Techniques - A Reservoir Management Approach. Journal of Petroleum Technology, 43(10), 1180–1188. https://doi.org/10.2118/23471-PA
Thakur, G. C. (1998). The Role of Reservoir Management in Carbonate Waterfloods. The SPE India Oil and Gas Conference and Exhibition, 56, 20–34. https://doi.org/10.2118/39519-MS
Wang, Z., He, J., Milliken, W. J., & Wen, X. H. (2021). Fast History Matching and Optimization Using a Novel Physics-Based Data-Driven Model: An Application to a Diatomite Reservoir. SPE Journal, 26(06), 4089–4108. https://doi.org/10.2118/200772-PA
Weber, D., Edgar, T. F., Lake, L. W., Lasdon, L., Kawas, S., & Sayarpour, M. (2009). Improvements in Capacitance-Resistive Modeling and Optimization of Large Scale Reservoirs. SPE Western Regional Meeting, 369–385. https://doi.org/10.2118/121299-MS
Willhite, G. P. (1986). Waterflooding. In Waterflooding. Society of Petroleum Engineers. https://doi.org/10.2118/9781555630058
Yousef, A. A., Gentil, P., Jensen, J. L., & Lake, L. W. (2006). A Capacitance Model To Infer Interwell Connectivity From Production- and Injection-Rate Fluctuations. SPE Reservoir Evaluation & Engineering, 9(06), 630–646. https://doi.org/10.2118/95322-PA
Zhao, H., Kang, Z., Zhang, X., Sun, H., Cao, L., & Reynolds, A. C. (2015, February 23). INSIM: A Data-Driven Model for History Matching and Prediction for Waterflooding Monitoring and Management with a Field Application. SPE Reservoir Simulation Symposium.
Zitha, P., Felder, R., Zornes, D., Brown, K., & Mohanty, K. (n.d.). Increasing Hydrocarbon Recovery Factors. Retrieved December 22, 2022, from https://www.spe.org/en/industry/increasing-hydrocarbon-recovery-factors/
Authors
Copyright (c) 2022 Billal Aslam, Hasto Nugroho, Fahriza Mahendra, Rani Kurnia, Taufan Marhaendrajana, Septoratno Siregar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. The copyright in the text of individual articles (including research articles, opinion articles, and abstracts) is the property of their respective authors, subject to a Creative Commons CC-BY-SA licence granted to all others. JEEE allows the author(s) to hold the copyright without restrictions and allows the author to retain publishing rights without restrictions.