Oil Reservoir Simulation via Deep Learning: Mini Review

Oil Reservoir Simulation via Deep Learning: Mini Review

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2022 by IJRES Journal
Volume-9 Issue-5
Year of Publication : 2022
Authors : Thlama Mperiju Mainta, Yahi Ali Dzakwa, Yakubu Ishaku
DOI : 10.14445/23497157/IJRES-V9I5P101

How to Cite?

Thlama Mperiju Mainta, Yahi Ali Dzakwa, Yakubu Ishaku, "Oil Reservoir Simulation via Deep Learning: Mini Review," International Journal of Recent Engineering Science, vol. 9, no. 5, pp. 1-10, 2022. Crossref, https://doi.org/10.14445/23497157/IJRES-V9I5P101

Abstract
Reservoir engineers are faced with constitutive reservoir estimation due to monumental data. In some cases, these datasets are difficult to analyze and extrapolate. Geological uncertainties can also affect the way reservoir data are managed. This intricacy has, in one way or the other, created many discrepancies in data utilization, determining how this reservoir data are incorporated into production forecasting. For this reason, data generated from these reservoir operations are used to obtain surrogate models via smart systems (Deep learning). This review aims to evaluate the systemic application of deep learning models to oil reservoir processes by considering various models, such as the Time series models. We first looked at the current trend of technological innovation in the oil and gas sector. We reviewed work done by several authors in different areas of reservoir modelling and simulation and how their works impact global oil and gas production by implementing smart technology. With the tremendous applicability of smart systems, oil reservoir management has become less complex due to automation. Artificial Neural Networks have been shown to improve the production efficiency of oil reservoirs even though geological uncertainties are inherent.

Keywords
Artificial Neural Networks, Data driven modelling, Enhanced oil recovery, Reservoir simulation.

Reference
[1] Berihun Mamo Negash, and Atta Dennis Yaw, “Artificial Neural Network Based Production Forecasting for a Hydrocarbon Reservoir Under Water Injection,” Petroleum Exploration and Development, vol. 47, no. 2, pp. 383–392, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Oluwafisayo Meshioye et al., “Optimization of Waterflooding Using Smart Well Technology,” Nigeria International Conference and Exhibition, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Lichun Kuang et al., “Application and Development Trend of Artificial Intelligence in Petroleum Exploration and Development,” Petroleum Exploration and Development, vol. 48, no. 1, pp. 1–14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Penghui Zhang et al., “Waterflooding Identification of Continental Clastic Reservoirs Based on Neural Network,” International Journal of Innovation and Applied Studies, vol. 4, no. 2, pp. 248–253, 2013.
[Google Scholar] [Publisher Link]
[5] Shams Kalam et al., “A Novel Empirical Correlation for Waterflooding Performance Prediction in Stratified Reservoirs using Artificial Intelligence,” Neural Computing and Applications, vol. 33, pp. 2497-2514, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jackson Udy et al., “Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms,” Processes, vol. 5, no. 3, p. 34, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] A.S. Grema et al., “Enhancing Oil Recovery through Waterflooding,” The Arid Zone Journal of Engineering, Technology and Environment, vol. 16, no. 3, pp. 561–568, 2020.
[Google Scholar] [Publisher Link]
[8] Mahlong Marvin Kida et al., “Neural Network Based Performance Evaluation of a Waterflooded Oil Reservoir,” The International Journal of Recent Engineering Science, vol. 8, no. 3, pp. 1–6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Rene Y. Choi et al., “Introduction to Machine Learning, Neural Networks, and Deep Learning,” Translational Vision Science & Technology, vol. 9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] G. Rzevski, “Artificial Intelligence in Engineering: Past, Present and Future,” Transaction on Information and Communication Technologies, vol. 10, 1995. [CrossRef] [Google Scholar] [Publisher Link]
[11] Jie Zhou et al., “Graph Neural Networks : A Review of Methods And Applications,” AI Open, vol. 1, pp. 57–81, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Rui Nian, Jinfeng Liu, and Biao Huang, “A Review on Reinforcement Learning : Introduction and Applications in Industrial Process Control Artificial Intelligence Constrained Markov Decision Process Dynamic Programming,” Computers and Chemical Engineering, vol. 139, p. 106886, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] N. Buduma and N. Locascio, “Deep Learning,” First. Sebastopol: O'Reilly Media Inc., 2017.
[14] Sandro Skansi, Introduction to Deep Learning from Logical Calculus to Artificial Intelligence, Cham, Switzerland: Springer, 2018.
[Google Scholar] [Publisher Link]
[15] Ismoilov Nusrat, and Sung-Bong Jang, “A Comparison of Regularization Techniques in Deep Neural Networks,” Symmetry, vol. 10, no. 11, p. 648, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Franck Dernoncourt, and Ji Young Lee, “Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification,” 2016 IEEE Spok. Language Technology Work, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hongqing Song et al., “Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods,” Geofluids, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Anirbid Sircar et al., “Application of Machine Learning and Artificial Intelligence in Oil and Gas Industry,” Petroleum Research, vol. 6, no. 4, pp. 379-391, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Lihui Tang et al., “Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network,” Geofluids, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammad Ali Ahmadi et al., “Determination of Oil Well Production Performance using Artificial Neural Network (ANN) Linked to the Particle Swarm Optimization (PSO) Tool,” Petroleum, vol. 1, no. 2, pp. 118–132, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[21] U. Chaudhry, Oil Well Testing Handbook, Burlington, USA: Gulf Professional Publishing, 2004.
[Google Scholar] [Publisher Link]
[22] Farid Ahmadloo, Koorosh Asghari, and Gay Renouf, “Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network,” Energy Fuels, vol. 24, no. 4, pp. 2520–2526, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mabkhout M. Al-dousari, and Ali A. Garrouch, “An Artificial Neural Network Model for Predicting the Recovery Performance of Surfactant Polymer Floods,” Journal of Petroleum Science and Engineering, vol. 109, pp. 51–62, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Ehsan Amirian, and Zhangxing John Chen, “Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Water-flooding Process Technology & Optimization,” Global Journal of Technology and Optimization, vol. 8, no. 1, pp. 1–8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Celio Maschio, and Denis Jose Schiozer, “A New Upscaling Technique Based on Dykstra–Parsons Coefficient: Evaluation with Streamline Reservoir Simulation,” Journal of Petroleum Science and Engineering, vol. 40, no. 1-2, pp. 27–36, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Yuanjun Li et al., “Dynamic Layered Pressure Map Generation in a Mature Waterflooding Reservoir Using Artificial Intelligence Approach,” SPE Western Regional Meeting, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Jinzi Liu, “Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods,” Geofluids, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Zhi Zhong et al., “Predicting Field Production Rates for Waterflooding using a Machine Learning-Based Proxy Model,” Journal of Petroleum Science and Engineering, vol. 194, p. 107574, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Cuthbert Shang Wui Ng, Ashkan Jahanbani Ghahfarokhi, and Menad Naid Amar, “Application of Nature‑Inspired Algorithms and Artificial Neural Network in Waterflooding Well Control Optimization,” Journal of Petroleum Exploration and Production Technology, vol. 11, pp. 3103-3127, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] A.S. Grema, and Y. Cao, “Optimal Feedback Control of Oil Reservoir Waterflooding Processes,” International Journal of Automation and Computing, vol. 13, pp. 73–80, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[31] A.S. Grema et al., “Optimization and Non-Linear Identification of Reservoir Water Flooding Process,” Arid Zone Journal of Engineering, Technology and Environment, vol. 13, no. 5, pp. 610–619, 2017.
[Google Scholar] [Publisher Link]
[32] Farzad Hourfar et al., “A Reinforcement Learning Approach for Waterflooding Optimization in Petroleum Reservoirs,” Engineering Applications of Artificial Intelligence, vol. 77, pp. 98–116, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Turgay Ertekin, and Qian Sun, “Artificial Intelligence Applications in Reservoir Engineering : A Status Check,” Energies, vol. 12, no. 5, p. 2897, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Menad Naid Amar, Nourddine Zeraibi, and Kheireddine Redouane, “Optimization of WAG Process Using Dynamic Proxy, Genetic Algorithm and Ant Colony Optimization,” Arabian Journal for Science and Engineering, vol. 43, pp. 6399–6412, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[35] J. Jalali, and S.D. Mohaghegh, “Reservoir Simulation and Uncertainty Analysis of Enhanced CBM Production Using Artificial Neural Network,” SPE Eastern Regional Meeting, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[36] W. Ampomah et al., “Optimum Design of CO2 Storage and Oil Recovery Under Geological Uncertainty,” Applied Energy, vol. 195, pp. 80–92, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Junyu You et al., “Assessment of Enhanced Oil Recovery and CO Storage Capacity Using Machine Learning and Optimization Framework,” The SPE Europic Featured at 81st EAGE Conference and Exhibition, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Jeremie Bruyelle, and Dominique Guérillot, “Optimization of Waterflooding Strategy Using Artificial Neural Networks,” The SPE Reservoir Characterisation and Simulation Conference and Exhibition, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Fahim Forouzanfar, Gaoming Li, and A.C. Reynolds, “A Two-Stage Well Placement Optimization Method Based on Adjoint Gradient,” The SPE Annual Technical Conference and Exhibition, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Chunhong Wang, Gaoming Li, and A.C. Reynolds, “Optimal Well Placement for Production Optimization,” SPE Eastern Regional Meeting, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Pallav Sarma, and Wen H. Chen, “Efficient Well Placement Optimization with Gradient-Based Algorithms and Adjoint Models,” SPE Intelligent Energy Conference and Exhibition, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[42] W. Bangerth et al., “On Optimization Algorithms for the Reservoir Oil Well Placement Problem,” Computational Geoscience, vol. 10, pp. 303–319, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Fahim Forouzanfar, and A.C. Reynolds, “Joint Optimization of Number of Wells, Well Locations and Controls using a Gradient-Based Algorithm,” Chemical Engineering Research and Design, vol. 92, no. 7, pp. 1315-1328, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[44] M.A. Dada et al., “A Field Development Strategy for the Joint Optimization of Flow Allocations, Well Placements and Well Trajectories,” Energy Exploration & Exploitation, vol. 39, no. 1, pp. 502–527, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Mohammad A. Al Dossary, and Hadi Nasrabadi, “Well Placement Optimization using Imperialist Competitive Algorithm,” Journal of Petroleum Science and Engineering, vol. 147, pp. 237–248, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Alireza Shahkarami et al., “Artificial Intelligence (AI) Assisted History Matching,” SPE Western North American and Rocky Mountain Joint Meeting, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[47] H. Doraisamy, T. Ertikin, and A.S. Grader, “Key Parameters Controlling the Performance of Neuro-Simulation Applications in Field Development,” SPE Eastern Regional Meeting, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[48] A. Centilmen, T. Ertekin, and A.S. Grader, “Applications of Neural Networks in Multiwell Field Development,” The SPE Annual Technical Conference and Exhibition, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[49] B.H. Min et al., “Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 33, no. 18, pp. 1726-1738, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[50] IIsik Jang et al., “Well-Placement Optimisation using Sequential Artificial Neural Networks,” Energy Exploration & Exploitation, vol. 36, no. 3, pp. 433–449, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Xinwei Xiong, and Kyung Jae Lee, “Data-Driven Modeling to Optimize the Injection Well Placement for Waterflooding in Heterogeneous Reservoirs Applying Artificial Neural Networks and Reducing Observation Cost,” Energy Exploration & Exploitation, vol. 38, no. 6, pp. 2413–2435, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Mahlon Marvin Kida, and Zakiyyu Muhamad Sarkinbaka, “Multivariate Optimization of a Jacketed Heating System : A Genetic Algorithm Approach,” International Journal of Recent Engineering Science, vol. 8, no. 2, pp. 20–25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Mahlon Kida Marvin, Aliyu Buba Ngulde, and Abdulhalim Musa Abubakar, “Pattern Effect for Oil Reservoir Waterflooding Using Smart Well,” Applied Engineering, vol. 6, no. 2, pp. 50–56, 2022.
[CrossRef] [Google Scholar] [Publisher Link]