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

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 the global production of oil and gas 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.

Artificial Neural Networks, Data Driven Modelling, Enhanced Oil Recovery, Reservoir Simulation.

[1] N. B. Mamo and Y. A. W. A. Dennis, "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. Doi: 10.1016/S1876-3804(20)60055-6.
[2] O. Meshioye, E. Mackay, E. Ekeoma, and M. Chukuwezi, "Optimization of Waterflooding Using Smart Well Technology," in 34th Annual SPE International Conference and Exhibition, pp. 9, 2010.
[3] K. Lichun 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. Doi: 10.1016/S1876-3804(21)60001-0.
[4] P. Zhang, J. Zhang, M. Li, M. Tang, and J. Li, "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.
[5] S. Kalam, S. A. Abu-khamsin, H. Y. Al-yousef, and R. Gajbhiye, "A Novel Empirical Correlation for Waterflooding Performance Prediction in Stratified Reservoirs using Artificial Intelligence," Neural Computing and Applications, vol. 1, pp. 18, 2020. Doi: 10.1007/s00521-020-05158-1.
[6] J. Udy et al., "Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms," Processes MDPI, vol. 5, no. 34, p. 25, 2017. Doi: 10.3390/pr5030034.
[7] A. S. Grema, M. K. Mahlon, U. H. Taura, and A. S. Kolo, "Enhancing Oil Recovery through Waterflooding," The Arid Zone Journal of Engineering, Technology and Environment, vol. 16, no. 3, pp. 561–568, 2020.
[8] M. M. Kida, Z. M. Sarkinbaka, A. M. Abubakar, and A. Z. Abdul, "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. Doi: 10.14445/23497157/IJRESV8I3P101.
[9] R. Y. Choi, A. S. Coyner, J. Kalpathy-cramer, M. F. Chiang, and J. P. Campbell, "Introduction to Machine Learning, Neural Networks, and Deep Learning," Translational Vision Science & Technology, vol. 9, no. 2, pp. 1–12, 2020. Doi: http://doi.org/10.1167/tvst.9.2.14.
[10] G. Rzevski, "Artificial Intelligence in Engineering: Past, Present and Future," Trans. Information and Communication Technologies, vol. 8, 1995.
[11] J. Zhou et al., "Graph Neural Networks : A Review of Methods And Applications," AI Open, vol. 1, pp. 57–81, 2021. Doi: 10.1016/j.aiopen.2021.01.001.
[12] R. Nian, J. Liu, and B. 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, pp. 106886, 2020. Doi: 10.1016/j.compchemeng.2020.106886.
[13] N. Buduma and N. Locascio, “Deep Learning,” First. Sebastopol: O'Reilly Media Inc., 2017.
[14] I. Mackie, “Introduction to Deep Learning from Logical Calculus to Artificial Intelligence,” Cham, Switzerland: Springer, 2018.
[15] I. Nusrat and S. Jang, "A Comparison of Regularization Techniques in Deep Neural Networks," Symmetry (Basel)., vol. 10, no. 648, pp. 1–18, 2018. Doi: 10.3390/sym10110648.
[16] F. Dernoncourt and J. Y. Lee, "Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification," 2016 IEEE Spok. Language Technology Work., pp. 406–413, 2016. Doi: 10.1109/SLT.2016.7846296.
[17] H. Song, S. Du, R. Wang, J. Wang, Y. Wang, and C. Wei, "Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods," Geofluids, pp. 12, 2020. Doi: https://doi.org/10.1155/2020/3713525.
[18] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, "Application of Machine Learning and Artificial Intelligence in Oil and Gas Industry," Petroleum Research, 2021. Doi: 10.1016/j.ptlrs.2021.05.009.
[19] L. Tang et al., "Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network," Geofluids, pp. 15, 2021. Doi: https://doi.org/10.1155/2021/8873782.
[20] M. Ali, R. Soleimani, M. Lee, T. Kashiwao, and A. Bahadori, "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. Doi: 10.1016/j.petlm.2015.06.004.
[21] A. U. Chaudhry, “Oil Well Testing Handbook,” Burlington, USA: Gulf Professional Publishing, 2004.
[22] F. Ahmadloo, K. Asghari, and G. Renouf, "Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network," Energy Fuels, vol. 24, no. 8, pp. 2520–2526, 2010. Doi: 10.1021/ef9013218.
[23] M. M. Al-dousari and A. 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. Doi: 10.1016/j.petrol.2013.08.012.
[24] E. Amirian and Z. J. 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. Doi: 10.4172/2229- 8711.1000207.
[25] C. Maschio and D. J. Schiozer, "A New Upscaling Technique Based on Dykstra–Parsons Coefficient: Evaluation with Streamline Reservoir Simulation," Journal of Petroleum Science and Engineering, vol. 40, pp. 27–36, 2003. Doi: 10.1016/S0920-4105(03)00060- 3.
[26] Y. Li, A. Popa, A. Johnson, and I. Ershaghi, "Dynamic Layered Pressure Map Generation in a Mature Waterflooding Reservoir Using Artificial Intelligence Approach," in SPE Western Regional Meeting, no. 1, pp. 1–14, 2018.
[27] J. Liu, "Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods," Geofluids, pp. 10, 2020. Doi: https://doi.org/10.1155/2020/1651549.
[28] Z. Zhong, A. Y. Sun, Y. Wang, and B. Ren, "Predicting Field Production Rates for Waterflooding using a Machine Learning-Based Proxy Model," Journal of Petroleum Science and Engineering, vol. 194, pp. 107574, 2020. Doi: 10.1016/j.petrol.2020.107574.
[29] C. N. W. Shang, A. G. Jahanbani, and A. N. Menad, "Application of Nature‑Inspired Algorithms and Artificial Neural Network in Waterflooding Well Control Optimization," Journal of Petroleum Exploration and Production Technology, vol. 10, 2021. Doi: 10.1007/s13202-021-01199-x.
[30] A. S. Grema and Y. Cao, "Optimal Feedback Control of Oil Reservoir Waterflooding Processes," International Journal of Automation and Computing, vol. 13, no. 1, pp. 73–80, 2016. Doi: 10.1007/s11633-015-0909-7.
[31] A. S. Grema, D. Baba, U. H. Taura, M. B. Grema, and L. T. Popoola, "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, 2018.
[32] F. Hourfar, H. B. Jalaly, B. Moshiri, K. Salahshoor, and A. Elkamel, "A Reinforcement Learning Approach for Waterflooding Optimization in Petroleum Reservoirs," Engineering Applications of Artificial Intelligence, vol. 77, no. May 2017, pp. 98–116, 2019. Doi: 10.1016/j.engappai.2018.09.019.
[33] T. Ertekin and Q. Sun, "Artificial Intelligence Applications in Reservoir Engineering : A Status Check," Energies, MDPI, vol. 12, pp. 2897, 2019. Doi: doi:10.3390/en12152897.
[34] A. N. Menad, N. Zeraibi, and K. 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. Doi: dx.doi.org/10.1007/s13369-018-3173-7.
[35] J. Jalali and S. Mohaghegh, "Reservoir Simulation and Uncertainty Analysis of Enhanced CBM Production Using Artificial Neural Network," 2009.
[36] W. Ampomah et al., "Optimum Design of CO2 Storage and Oil Recovery Under Geological Uncertainty," Applied Energy, vol. 195, pp. 80–92, 2017. Doi: dx.doi.org/10.1016/j.apenergy.2017.03.017.
[37] J. You et al., "Assessment of Enhanced Oil Recovery and CO Storage Capacity Using Machine Learning and Optimization Framework," 2019.
[38] J. Bruyelle and D. Guérillot, "Optimization of Waterflooding Strategy Using Artificial Neural Networks," Society of Petroleum Engineers, pp. 17–19, 2019.
[39] F. Forouzanfar, G. Li, and A. C. Reynolds, "A Two-Stage Well Placement Optimization Method Based on Adjoint Gradient," in 2010 SPE Annual Technical Conference and Exhibition, no. September, pp. 20–22, 2010.
[40] C. Wang, G. Li, and A. C. Reynolds, "Optimal Well Placement for Production Optimization," in SPE Eastern Regional Meeting, pp. 11-14, 2007.
[41] P. Sarma and W. H. Chen, "Efficient Well Placement Optimization with Gradient-Based Algorithms and Adjoint Models," in SPE Intelligent Energy Conference and Exhibition, pp. 18, 2008.
[42] W. Bangerth, H. Klie, M. F. Wheeler, P. L. Stoffa, and M. K. Sen, "On Optimization Algorithms for the Reservoir Oil Well Placement Problem," Comput Geoscience, vol. 10, pp. 303–319, 2006. Doi: 10.1007/s10596-006-9025-7.
[43] F. 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, no. September 2012, pp. 1–14, 2013. Doi: 10.1016/j.cherd.2013.11.006.
[44] M. A. Dada, M. Mellal, and A. Makhloufi, "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. Doi: 10.1177/0144598720974425.
[45] M. A. Al Dossary and H. Nasrabadi, "Well Placement Optimization using Imperialist Competitive Algorithm," Journal of Petroleum Science and Engineering, vol. 147, pp. 237–248, 2016, doi: 10.1016/j.petrol.2016.06.017.
[46] A. Shahkarami, S. D. Mohaghegh, V. Gholami, and S. A. Haghighat, "Artificial Intelligence (AI) Assisted History Matching," in SPE Western North American and Rocky Mountain Joint Regional Meeting, pp. 16-18, 2014.
[47] H. Doraisamy, T. Ertikin, and A. S. Grader, "Key Parameters Controlling the Performance of Neuro-Simulation Applications in Field Development," in SPE Eastern Regional Meeting, pp. 233-241, 1998.
[48] A. Centilmen, T. Ertekin, and A. S. Grader, "Applications of Neural Networks in Multiwell Field Development," 1999.
[49] B. H. Min, C. Park, J. M. Kang, H. J. Park, and I. S. Jang, "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. 37–41, 2011. Doi: 10.1080/15567030903468569.
[50] I. Jang, S. Oh, Y. Kim, C. Park, and H. Kang, "Well-Placement Optimisation using Sequential Artificial Neural Networks," Energy Exploration & Exploitation, vol. 36, no. 3, pp. 433–449, 2018, doi: 10.1177/0144598717729490.
[51] X. Xiong and K. J. 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. Doi: 10.1177/0144598720927470.
[52] M. M. Kida and Z. M. 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. Doi: 10.14445/23497157/IJRES-V8I2P104.
[53] M. K. Marvin, A. B. Ngulde, and A. M. Abubakar, "Pattern Effect for Oil Reservoir Waterflooding Using Smart Well," Applied Engineering, vol. 6, no. 2, pp. 50–56, 2022. Doi: 10.11648/j.ae.20220602.13.