Selecting the Best Pilot Area for Water-based EOR Using Artificial Intelligence and Multi Criteria Decision Making Algorithms

Document Type : Research Paper

Authors

Faculty of Petroleum Engineering, Sahand University of Technology, Tabriz, Iran

10.22078/pr.2024.5315.3361

Abstract

Selecting the best candidate pilot area is one of the most important and challenges decisions in the oil and gas filed development plan. Pilot-scale projects are conducted to reduce reservoir uncertainties and investment risk and the lesson learned from this study will be extended to the full field implementation. The main objective of this study is to utilize sseveral geological, operational and economic criteria to make decisions optimally among candidate areas. Firstly, reservoir similarity index (RSI) is calculated using the oil production history, saturation data. For this reason, clustering methods including k-means, k-medoids and c-means are used to identify the center of the dominant cluster. Afterwards, other operational criteria such as the number of interference, adjacent wells, the average distance between these wells and area center and the average distance from facilities are determined for all candidate areas. Finally, the decision matrix is created and then multi-criteria decision making (MCDM) methods are utilized to calculate pilot opportunity index for each area. According to the obtained results, the assigned pilot opportunity index of the hierarchical analysis method was equal to 11.50, 10.46 and 6.97%, and for the Shannon entropy method it was equal to 11.67, 9.80 and 6.80%, respectively for the top three pilot areas. The area with the highest value is selected as the first rank candidate for pilot implementation. Moreover, mean rank method is utilized to aggregate and introduce the best pilot area. 

Keywords

Main Subjects

References

[1]. پروازدوانی، م.، مطهری س. م. و گلقندشتی ح. (1396). طراحی پایلوت ازدیاد برداشت- چالش‌ها و راه‌کارها. ماه‌نامه علمی اکتشاف و تولید نفت و گاز. (147): 29-21.##
[2]. Motahhari, S. M., Rafizadeh, M., Pishvaie, S. M. R., & Ahmadi, M. (2021). An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles, 76, 82, doi: 10.2516/ogst/2021061. ##
[3]. Teletzke, G. F., Wattenbarger, R. C., & Wilkinson, J. R. (2010). Enhanced oil recovery pilot testing best practices. SPE Reservoir Evaluation & Engineering, 13(01), 143-154, doi: 10.2118/118055-ms. ##
[4]. مطهری، س. م.، رفیع‌زاده، م.، پیشوائی، س. م. و احمدی, م. (2021). انتخاب مکان پیاده‌سازی پایلوت ازدیاد برداشت در توسعه میادین هیدروکربنی بالغ با استفاده از تلفیق روش‌های تاپسیس و خوشه‌بندی هیبریدی. پژوهش نفت, 31(1400–3), 3–16. doi:10.22078/pr.2020.4303.2951##
[5]. Kheirollahi, H., Chahardowli, M., & Simjoo, M. (2022). A new method of well clustering and association rule mining. Journal of Petroleum Science and Engineering, 214, 110479, doi: 10.1016/j.petrol.2022.110479. ##
[6]. Delamaide, E., Soe Let, K. M., Bhoendie, K., Jong-A-Pin, S., & Paidin, W. R. (2016, June). Results of a polymer flooding pilot in the Tambaredjo heavy oil field, Suriname. In SPE Canada Heavy Oil Conference (p. D011S001R005). SPE, doi: 10.2118/180739-ms. ##
[7]. Ning, S., Barnes, J., Edwards, R., Schulpen, W., Dandekar, A., Zhang, Y., Cercone, D. & Ciferno, J. (2020). First ever polymer flood field pilot to enhance the recovery of heavy oils on Alaska North Slope–producer responses and operational lessons learned. In SPE Annual Technical Conference and Exhibition? (p. D031S029R004). SPE, doi: 10.2118/201279-ms. ##
[8]. Irvine, R., Davidson, J., Edwards, S., Kingsbury, J., Hui-June, P., & Tardiff, C. (2012, April). Case study of polymer flood pilot in a low permeability Mannville sand of the Western Canadian sedimentary basin using produced water for blending. In SPE Improved Oil Recovery Conference? (pp. SPE-154050). SPE., doi: 10.2118/154050-ms. ##
[9]. Sheshbolouky, M., Kheirollahi, H., & Khodapanah, E. (2024). Using multi-objective optimization genetics algorithm for Co-estimation of saturation functions by piecewise model. Journal of Petroleum Research, 34(1403-2), 72-90, doi: 10.22078/pr.2024.5266.3337. ##
[10]. Gao, C. H. (2011). Scientific research and field applications of polymer flooding in heavy oil recovery. Journal of Petroleum Exploration and Production Technology, 1, 65-70, doi: 10.1007/s13202-011-0014-6. ##
[11]. Al-Dhuwaihi, A. S., Abdullah, M. B., Tiwari, S., Al-Murayri, M. T., Al-Mayyan, H., Shahin, G. T., & Shukla, S. (2017, October). Fit-for-purpose chemical EOR ASP modeling strategy to guide pilot development decisions for a giant reservoir in North Kuwait. In SPE Asia Pacific Oil and Gas Conference and Exhibition (p. D021S009R003). SPE., doi: 10.2118/186351-ms. ##
[12]. Koning, E. J. L., Mentzer, E., & Heemskerk, J. (1988, October). Evaluation of a pilot polymer flood in the Marmul field, Oman. In SPE Annual Technical Conference and Exhibition? (pp. SPE-18092). SPE., doi: 10.2118/18092-ms. ##
[13]. Subbalakshmi, C., Krishna, G. R., Rao, S. K. M., & Rao, P. V. (2015). A method to find optimum number of clusters based on fuzzy silhouette on dynamic data set. Procedia Computer Science, 46, 346-353, doi: 10.1016/j. procs.2015.02.030. ##
[14]. Wicaksana A. & Rachman T. (2010). MAKING GOOD DECISIONS, 3, 1. ##
[15]. Rajabi, M., Kord, S., Hashemi, A. N., & Salehi, R. (2020). Screening of Sand Control Methods for One Well in South Western of Iran Using TOPSIS Algorithm. Journal of Petroleum Research, 30(99-4), 105-117, doi: 10.22078/pr.2020.4174.2891. ##
[16]. Khojastehmehr, M., Madani, M., & Daryasafar, A. (2019). Screening of enhanced oil recovery techniques for Iranian oil reservoirs using TOPSIS algorithm. Energy Reports, 5, 529-544.doi.org/10.1016/j.egyr.2019.04.011. ##
[17]. نعمتیان ج.، باحجب م. و طباطبایی س. (1400). مقدمه‌ای بر نظریه تصمیم‌گیری. تبریز: انتشارات دانشگاه تبریز. ##
 
Journal of Petroleum Research
Volume 34, 1403-5 - Serial Number 138
January and February 2025
Pages 3-19

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