Using an Ensembled Artificial Intelligence Approach for EOR Methods Screening in Oil Fields

Document Type : Research Paper

Authors

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

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

Abstract

Decision-making to choose the best Enhanced Oil Recovery (EOR) method(s) among different variety of models, is a vital step in the oil reservoir development process. Selecting the proper EOR method has a key role in the technical and economic success of enormous oil industry projects. Screening criteria are used for selecting the best EOR method(s) for an oil reservoir with specific rock and fluid properties. The main input parameters that affect the screening process include; Reservoir capacity, fluid transmissibility and permeability, depth, net thickness, temperature, and oil gravity (API). Considering mentioned parameters have uncertainty, specifying a suitable EOR method for reservoir development is a radical challenge. It can be used combination of fuzzy logic systems (knowledge base) and artificial neural networks (data-driven) as a suitable tool and solution for expressing uncertainty and screening methods. In this study, data from the history of different reservoirs worldwide is used to define fuzzy variables and determine fuzzy rules between input and target variables, and finally, a fuzzy model and a single-layer neural network with 20 neurons are presented. ANN model provides 92% accuracy in the prediction of the target method. Consequently, we proposed the ensemble model for the selection of the EOR screening tool.

Keywords

Main Subjects

References

[1]. Thomas, S. (2008). Enhanced oil recovery-an overview, Oil & Gas Science and Technology-Revue de l’IFP, 63(1), 9-19, doi.org/10.2516/ogst:2007060 . ##
[2]. Mahdavi, E., & Zebarjad, F. (2018). Screening criteria of enhanced oil recovery methods, Fundamentals of Enhanced Oil and Gas Recovery from Conventional and Unconventional Reservoirs, Amsterdam: Elsevier, 41-59, doi:10.1016/B978-0-12-813027-8.00002-3. ##
[3]. Al Adasani, A., & Bai, B. (2011). Analysis of EOR projects and updated screening criteria. Journal of Petroleum Science and Engineering, 79(1-2), 10-24, https://doi.org/10.1016/j.petrol.2011.07.005. ##
[4]. Lee, J. Y., Shin, H. J., & Lim, J. S. (2011). Selection and evaluation of enhanced oil recovery method using artificial neural network. Geosystem Engineering, 14(4), 157-164, https://doi.org/10.1080/12269328.2011.10541345. ##
[5]. Ibatullin, R. R., Ibragimov, N. G., Khisamov, R. S., Podymov, E. D., & Shutov, A. A. (2002, April). Application and method based on artificial intelligence for selection of structures and screening of technologies for enhanced oil recovery, In SPE Improved Oil Recovery Conference? SPE-7517 doi.org/10.2118/75175-MS. ##
[6]. Taber, J. J. (1983, October). Technical screening guides for the enhanced recovery of oil, In SPE Annual Technical Conference and Exhibition, OnePetro, doi.org/10.2118/12069-MS. ##
[7]. Guerillot, D. R. (1988, June). EOR screening with an expert system, In SPE Petroleum Computer Conference, SPE-17791, doi.org/10.2118/17791-MS. ##
[8]. Parkinson, W. J., Luger, G. F., Bretz, R. E., & Osowski, J. (1994). Using an expert system to explore enhanced oil recovery methods, Computers & Electrical Engineering, 20(2), 181-197, doi.org/10.1016/0045-7906(94)90029-9. ##
[9]. 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. ##
[10]. Parada, C. H., & Ertekin, T. (2012). A new screening tool for improved oil recovery methods using artificial neural networks, In SPE Western Regional Meeting. OnePetro, doi.org/10.2118/153321-MS. ##
[11]. Siena, M., Guadagnini, A., Rossa, E. D., Lamberti, A., Masserano, F., & Rotondi, M. (2016). A novel enhanced-oil-recovery screening approach based on Bayesian clustering and principal-component analysis, SPE Reservoir Evaluation & Engineering, 19(03), 382-390, doi.org/10.2118/174315-PA. ##
[12]. Siena, M., Guadagnini, A., Rossa, E. D., Lamberti, A., Masserano, F., & Rotondi, M. (2016). A novel enhanced-oil-recovery screening approach based on Bayesian clustering and principal-component analysis, SPE Reservoir Evaluation & Engineering, 19(03), 382-390, doi.org/10.2118/174315-PA. ##
[13]. Ramos, G. A., & Akanji, L. (2017). Data analysis and neuro-fuzzy technique for EOR screening: Application in Angolan oilfields. Energies, 10(7), 837, doi.org/10.3390/en10070837. ##
[14]. 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.org/10.22078/pr.2020.4174.2891. ##
[15]. Pirizadeh, M., Alemohammad, N., Manthouri, M., & Pirizadeh, M. (2021). A new machine learning ensemble model for class imbalance problem of screening enhanced oil recovery methods, Journal of Petroleum Science and Engineering, 198, 108214, doi.org/10.1016/j.petrol.2020.108214. ##
[16]. Cheraghi, Y., Kord, S., & Mashayekhizadeh, V. (2021). Application of machine learning techniques for selecting the most suitable enhanced oil recovery method; challenges and opportunities. Journal of Petroleum Science and Engineering, 205, 108761, doi.org/10.1016/j.petrol.2021.108761. ##
[17]. Fereidooni, A., Fereidooni, M., Moradi, S., Zargar, G., & Ganjeh Ghazvini, M. (2014). A screening analysis of conventional reservoirs for nitrogen and dry gas injection by using a combination of experimental design techniques and compositional reservoir simulation, Journal of Petroleum Research, 24(79), 135-146. doi: 10.22078/pr.2014.414. ##
[18]. Alavala C. R. (2017). Fuzzy Logic and Neural Networks Basic Concepts and applications, Vol. 5, No. 1. ISBN: 978-81-224-2182-8. ##
Journal of Petroleum Research
Volume 33, 1402-5 - Serial Number 132
January and February 2024
Pages 51-62

Files

Share

How to cite

Statistics