{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:11:06Z","timestamp":1780416666472,"version":"3.54.1"},"reference-count":124,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.asoc.2026.115222","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:37:35Z","timestamp":1776443855000},"page":"115222","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A review of artificial intelligence techniques applied to subsurface oil and gas reservoir simulation"],"prefix":"10.1016","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1733-1677","authenticated-orcid":false,"given":"Shadfar","family":"Davoodi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Makarov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-4069","authenticated-orcid":false,"given":"David A.","family":"Wood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Shargabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Vanovskiy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry","family":"Koroteev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeriy S.","family":"Rukavishnikov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0690","authenticated-orcid":false,"given":"Evgeny","family":"Burnaev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115222_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.pce.2024.103846","article-title":"Integrated reservoir characterization and simulation approach to enhance production of tight sandstone gas reservoir, Sulige gas field, Ordos Basin, China","volume":"138","author":"Iqbal","year":"2025","journal-title":"Phys. Chem. Earth, Parts A\/B\/C"},{"key":"10.1016\/j.asoc.2026.115222_bib0010","series-title":"Reservoir Analysis Market Report 2025","year":"2025"},{"key":"10.1016\/j.asoc.2026.115222_bib0015","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.3390\/en12152897","article-title":"Artificial intelligence applications in reservoir engineering: a status check","volume":"12","author":"Ertekin","year":"2019","journal-title":"Energies"},{"key":"10.1016\/j.asoc.2026.115222_bib0020","series-title":"Reservoir Modelling: A Practical Guide","author":"Cannon","year":"2018"},{"key":"10.1016\/j.asoc.2026.115222_bib0025","first-page":"343","article-title":"Reservoir modeling & simulation: advancements, challenges, and future perspectives","volume":"57","author":"Khalili","year":"2023","journal-title":"J. Chem. Pet. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0030","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s10462-024-10865-5","article-title":"A comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions, Artif","volume":"57","author":"Rahmanifard","year":"2024","journal-title":"Intell. Rev."},{"key":"10.1016\/j.asoc.2026.115222_bib0035","series-title":"Reservoir engineering market by size, share and forecast 2030f","author":"LLC","year":"2020"},{"key":"10.1016\/j.asoc.2026.115222_bib0040","series-title":"Abu Dhabi International Petroleum Exhibition & Conference","article-title":"Embracing the digital and artificial intelligence revolution for reservoir management - intelligent integrated subsurface modelling IISM","author":"Mawlad","year":"2019"},{"key":"10.1016\/j.asoc.2026.115222_bib0045","doi-asserted-by":"crossref","first-page":"65","DOI":"10.46690\/ager.2023.10.07","article-title":"Artificial intelligence methods for oil and gas reservoir development: current progresses and perspectives","volume":"10","author":"Xue","year":"2023","journal-title":"Adv. Geo-Energy Res."},{"key":"10.1016\/j.asoc.2026.115222_bib0050","article-title":"Multi-objective optimization of reservoir development strategy with hybrid artificial intelligence method, expert syst","volume":"241","author":"Zhuang","year":"2024","journal-title":"Appl."},{"key":"10.1016\/j.asoc.2026.115222_bib0055","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.jngse.2011.08.003","article-title":"Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM)","volume":"3","author":"Mohaghegh","year":"2011","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0060","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S1876-3804(20)60041-6","article-title":"Subsurface analytics: contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management","volume":"47","author":"MOHAGHEGH","year":"2020","journal-title":"Pet. Explor. Dev."},{"key":"10.1016\/j.asoc.2026.115222_bib0065","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/en16031392","article-title":"Insights into the application of machine learning in reservoir engineering: current developments and future trends","volume":"16","author":"Wang","year":"2023","journal-title":"Energies"},{"key":"10.1016\/j.asoc.2026.115222_bib0070","doi-asserted-by":"crossref","first-page":"6727","DOI":"10.3390\/en16186727","article-title":"Applications of machine learning in subsurface reservoir simulation\u2014a review\u2014part II","volume":"16","author":"Samnioti","year":"2023","journal-title":"Energies (Basel)."},{"key":"10.1016\/j.asoc.2026.115222_bib0075","doi-asserted-by":"crossref","first-page":"6079","DOI":"10.3390\/en16166079","article-title":"Applications of machine learning in subsurface reservoir simulation\u2014a review\u2014part i","volume":"16","author":"Samnioti","year":"2023","journal-title":"Energies (Basel)."},{"key":"10.1016\/j.asoc.2026.115222_bib0080","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.3390\/pr12061219","article-title":"Machine learning in reservoir Engineering: a review","volume":"12","author":"Zhou","year":"2024","journal-title":"Processes"},{"key":"10.1016\/j.asoc.2026.115222_bib0085","article-title":"Artificial intelligence for reservoir modeling and property estimation in petroleum engineering, Physics and Chemistry of the earth","volume":"140","author":"Aljehani","year":"2025","journal-title":"Parts A\/B\/C"},{"key":"10.1016\/j.asoc.2026.115222_bib0090","doi-asserted-by":"crossref","first-page":"167999","DOI":"10.1109\/ACCESS.2025.3614017","article-title":"Reservoir simulations: a comparative review of machine learning approaches","volume":"13","author":"Zeedan","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115222_bib0095","article-title":"Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.asoc.2026.115222_bib0100","series-title":"Les Fontaines Publiques De La Ville De Dijon","author":"Darcy","year":"1856"},{"key":"10.1016\/j.asoc.2026.115222_bib0105","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1098\/rsta.1911.0009","article-title":"The approximate arithmetical solution by finite differences of physical problems involving differential equations, with an application to the stresses in a masonry DAM","volume":"210","year":"1911","journal-title":"Philos. Trans. R. Soc. Lond."},{"key":"10.1016\/j.asoc.2026.115222_bib0110","doi-asserted-by":"crossref","first-page":"269","DOI":"10.4236\/ajcm.2018.83022","article-title":"Application of differential transformation method to boundary value problems of order seven and eight","volume":"8","author":"Ogunrinde","year":"2018","journal-title":"Am. J. Comput. Math."},{"key":"10.1016\/j.asoc.2026.115222_bib0115","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2118\/942107-G","article-title":"Mechanism of fluid displacement in sands","volume":"146","author":"Buckley","year":"1942","journal-title":"Trans. AIME"},{"key":"10.1016\/j.asoc.2026.115222_bib0120","doi-asserted-by":"crossref","first-page":"215","DOI":"10.2118\/950215-G","article-title":"Liquid-liquid displacement in porous media as affected by the liquid-liquid viscosity ratio and liquid-liquid miscibility","volume":"2","author":"Everett","year":"1950","journal-title":"J. Pet. Technol."},{"key":"10.1016\/j.asoc.2026.115222_bib0125","first-page":"171","article-title":"Reservoir simulation: past, present, and future","volume":"9","author":"Watts","year":"1997","journal-title":"SPE Comput. Appl."},{"key":"10.1016\/j.asoc.2026.115222_bib0130","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1002\/cpa.3160070112","article-title":"Weak solutions of nonlinear hyperbolic equations and their numerical computation, commun","volume":"7","author":"Lax","year":"1954","journal-title":"Pure Appl. Math."},{"key":"10.1016\/j.asoc.2026.115222_bib0135","series-title":"A method for general reservoir behavior simulation on digital computers","author":"Sheldon","year":"1960"},{"key":"10.1016\/j.asoc.2026.115222_bib0140","doi-asserted-by":"crossref","first-page":"211","DOI":"10.2118\/2015-PA","article-title":"Three-phase reservoir simulation","volume":"21","author":"Peery","year":"1969","journal-title":"J. Pet. Technol."},{"key":"10.1016\/j.asoc.2026.115222_bib0145","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.2118\/2790-PA","article-title":"Reservoir simulation \u2026 what is it","volume":"21","author":"Odeh","year":"1969","journal-title":"J. Pet. Technol."},{"key":"10.1016\/j.asoc.2026.115222_bib0150","series-title":"Society of Petroleum Engineers - Fall Meeting of the Society of Petroleum Engineers of AIME, FM 1973","article-title":"Method for automatic history matching of reservoir simulation models","author":"Sol\u00f3rzano","year":"1973"},{"key":"10.1016\/j.asoc.2026.115222_bib0155","series-title":"Symposium on Petroleum Economics and Evaluation","article-title":"Economic aspects of application of computers to reservoir engineering","author":"Irby","year":"1965"},{"key":"10.1016\/j.asoc.2026.115222_bib0160","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.2118\/3304-PA","article-title":"Reservoir simulation models an engineering overview","volume":"23","author":"Staggs","year":"1971","journal-title":"J. Pet. Technol."},{"key":"10.1016\/j.asoc.2026.115222_bib0165","first-page":"302","article-title":"Probabilistic evaluation technique, in: methods and models for assessing energy resources","author":"Seigneurin","year":"1979","journal-title":"Pergamon"},{"key":"10.1016\/j.asoc.2026.115222_bib0170","series-title":"The 21st U.S. Symposium on Rock Mechanics (USRMS), OnePetro","article-title":"Analysis of the spatial variation in rock mass properties through geostatistics","author":"Pointe","year":"1980"},{"key":"10.1016\/j.asoc.2026.115222_bib0175","first-page":"269","article-title":"Spatial variability and uncertainty in groundwater flow parameters: a geostatistical approach, water resour","volume":"15","author":"Delhomme","year":"1979","journal-title":"Res."},{"key":"10.1016\/j.asoc.2026.115222_bib0180","doi-asserted-by":"crossref","first-page":"363","DOI":"10.2118\/8284-PA","article-title":"An equation of state compositional model","volume":"20","author":"Coats","year":"1980","journal-title":"Soc. Pet. Eng. J."},{"key":"10.1016\/j.asoc.2026.115222_bib0185","series-title":"AAPG International Conference and Exhibition, 2018, Cidade Do Cabo","first-page":"1","article-title":"High-performance reservoir simulations on modern CPU-GPU computational platforms","author":"Bogachev","year":"2018"},{"key":"10.1016\/j.asoc.2026.115222_bib0190","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/8876153","article-title":"A digital twin for unconventional reservoirs: a multiscale modeling and algorithm to investigate complex mechanisms","volume":"2020","author":"Zhang","year":"2020","journal-title":"Geofluids"},{"key":"10.1016\/j.asoc.2026.115222_bib0195","author":"Ducros"},{"key":"10.1016\/j.asoc.2026.115222_bib0200","article-title":"The design of an open-source carbonate reservoir model","volume":"28","author":"Gomes","year":"2022","journal-title":"Pet. Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0210","article-title":"CO2 injection modeling in a reservoir: phase transition study","author":"Omena","year":"2024","journal-title":"Proc. Ibero-Latin Am. Congr. Comput. Methods Eng. (CILAMCE)"},{"key":"10.1016\/j.asoc.2026.115222_bib0215","series-title":"Society of Petroleum Engineers - SPE Reservoir Simulation Symposium 2013","first-page":"1417","article-title":"Chemical reaction modeling in a compositional reservoir-simulation framework","volume":"vol. 2","author":"Farshidi","year":"2013"},{"key":"10.1016\/j.asoc.2026.115222_bib0220","series-title":"ECLIPS user manual release 7.1","year":"1990"},{"key":"10.1016\/j.asoc.2026.115222_bib0225","unstructured":"Rock flow Dynamics, tNavigator user manual release 25.1, 2021."},{"key":"10.1016\/j.asoc.2026.115222_bib0230","author":"D\u00fcrrenmatt"},{"key":"10.1016\/j.asoc.2026.115222_bib0235","series-title":"An introduction to reservoir simulation using MATLAB\/GNU octave\u2009: user guide for the MATLAB reservoir simulation toolbox (MRST)","author":"Lie","year":"2019"},{"key":"10.1016\/j.asoc.2026.115222_bib0245","series-title":"Fossil Energy Boast: A Three-Dimensional, Three-Phase Black Oil Applied Simulation Tool (Version 1.1)","author":"Fanchi","year":"1982"},{"key":"10.1016\/j.asoc.2026.115222_bib0255","article-title":"Petroleum geostatistics","author":"Caers","year":"2005","journal-title":"Soc. Pet. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0260","series-title":"Well testing","author":"Lee","year":"1982"},{"key":"10.1016\/j.asoc.2026.115222_bib0265","series-title":"Production Logging: Theoretical and Interpretive Elements","author":"Hill","year":"2021"},{"key":"10.1016\/j.asoc.2026.115222_bib0270","series-title":"Proceedings - SPE International Symposium on Oilfield Chemistry","first-page":"753","article-title":"A field case study of inter-well chemical tracer test","volume":"vol. 2","author":"Sanni","year":"2015"},{"key":"10.1016\/j.asoc.2026.115222_bib0275","series-title":"17th International Congress of the Brazilian Geophysical Society","first-page":"1","article-title":"Reservoir simulation to seismic: an approach for time lapse analysis","author":"de Matos","year":"2021"},{"key":"10.1016\/j.asoc.2026.115222_bib0280","first-page":"1","article-title":"A review of parallel computing for large-scale reservoir numerical simulation","author":"Meng","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0285","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.petlm.2018.11.001","article-title":"Big data analytics in oil and gas industry: an emerging trend","volume":"6","author":"Mohammadpoor","year":"2020","journal-title":"Petroleum"},{"key":"10.1016\/j.asoc.2026.115222_bib0290","article-title":"Performance evaluation of outlier detection techniques in production timeseries: a systematic review and meta-analysis, expert syst","volume":"191","author":"Alimohammadi","year":"2022","journal-title":"Appl."},{"key":"10.1016\/j.asoc.2026.115222_bib0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.upstre.2023.100098","article-title":"A review of modern approaches of digitalization in oil and gas industry","volume":"11","author":"Al-Rbeawi","year":"2023","journal-title":"Upstream Oil Gas Technol."},{"key":"10.1016\/j.asoc.2026.115222_bib0300","series-title":"Middle East Oil Show and Conference","first-page":"245","article-title":"Lithology, lithofacies, and permeability estimation in Ghawar Arab-D reservoir","author":"Davis","year":"1997"},{"key":"10.1016\/j.asoc.2026.115222_bib0305","first-page":"16","article-title":"Extended Brugge benchmark case for history matching and water flooding optimization, comput","volume":"50","author":"Peters","year":"2013","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0310","first-page":"4","article-title":"Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation, comput","volume":"50","author":"Arnold","year":"2013","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.geoen.2024.213597","article-title":"Hybrid RGNet: a physics-based model enhanced by machine learning for history matching, characterization and future prediction","volume":"246","author":"Guo","year":"2025","journal-title":"Geoenergy Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0320","author":"Santoso"},{"key":"10.1016\/j.asoc.2026.115222_bib0325","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.2118\/209611-PA","article-title":"Fast history matching and robust optimization using a novel Physics-Based Data-Driven flow network model: an application to a steamflood sector model","volume":"27","author":"Wang","year":"2022","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0330","first-page":"192","article-title":"The egg model \u2013 a geological ensemble for reservoir simulation, geosci","volume":"1","author":"Jansen","year":"2014","journal-title":"Data J."},{"key":"10.1016\/j.asoc.2026.115222_bib0340","series-title":"Economic Decision Analysis","author":"Jafarizadeh","year":"2022"},{"key":"10.1016\/j.asoc.2026.115222_bib0345","series-title":"Petrel geological model of the Groningen gas field, the Netherlands","year":"2020"},{"key":"10.1016\/j.asoc.2026.115222_bib0350","author":"Wiki"},{"key":"10.1016\/j.asoc.2026.115222_bib0365","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2019.106223","article-title":"A realistic and public dataset with rare undesirable real events in oil wells","volume":"181","author":"Vargas","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0370","series-title":"SPE Western Regional Meeting","first-page":"1","article-title":"Data-Driven characterization of shale reservoirs towards facilitation of production performance evaluation","author":"Salahshoor","year":"2021"},{"key":"10.1016\/j.asoc.2026.115222_bib0375","author":"Ertekin"},{"key":"10.1016\/j.asoc.2026.115222_bib0380","series-title":"The practice of reservoir engineering","first-page":"571","author":"Dake","year":"2008"},{"key":"10.1016\/j.asoc.2026.115222_bib0385","first-page":"185","article-title":"Recent progress on reservoir history matching: a review, comput","volume":"15","author":"Oliver","year":"2011","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0390","first-page":"3","article-title":"Ensemble smoother with multiple data assimilation, comput","volume":"55","author":"Emerick","year":"2013","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.jappgeo.2024.105501","article-title":"Permeability prediction using logging data from tight reservoirs based on deep neural networks","volume":"229","author":"Fang","year":"2024","journal-title":"J. Appl. Geophy."},{"key":"10.1016\/j.asoc.2026.115222_bib0400","series-title":"Assisted History Matching for Unconventional Reservoirs","author":"Tripoppoom","year":"2021"},{"key":"10.1016\/j.asoc.2026.115222_bib0405","doi-asserted-by":"crossref","first-page":"538","DOI":"10.2118\/102048-PA","article-title":"Production-data analysis\u2014challenges, pitfalls, diagnostics","volume":"13","author":"Ilk","year":"2010","journal-title":"SPE Reserv. Eval. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0410","series-title":"ECMOR 2020 - 17th European Conference on the Mathematics of Oil Recovery, European Association of Geoscientists and Engineers, EAGE","first-page":"1","article-title":"How does the definition of the objective function influence the outcome of history matching?","author":"Eremyan","year":"2020"},{"key":"10.1016\/j.asoc.2026.115222_bib0415","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1002\/widm.1059","article-title":"Objective function-based clustering","volume":"2","author":"Hall","year":"2012","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"10.1016\/j.asoc.2026.115222_bib0420","first-page":"16","article-title":"Geological feature selection in reservoir modelling and history matching with multiple kernel learning, comput","volume":"85","author":"Demyanov","year":"2015","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0425","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s11081-022-09726-3","article-title":"Improved feature selection with simulation optimization","volume":"24","author":"Shashaani","year":"2023","journal-title":"Optim. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0430","series-title":"SPE Reservoir Simulation Conference","article-title":"Optimal timestep selection in numerical reservoir simulation using a machine learning approach","author":"Al-Ruwai","year":"2025"},{"key":"10.1016\/j.asoc.2026.115222_bib0435","author":"Bishop"},{"key":"10.1016\/j.asoc.2026.115222_bib0440","series-title":"Algorithms for Optimization","author":"Kochenderfer","year":"2019"},{"key":"10.1016\/j.asoc.2026.115222_bib0445","doi-asserted-by":"crossref","first-page":"521","DOI":"10.2118\/13931-PA","article-title":"History matching by spline approximation and regularization in single-phase areal reservoirs","volume":"1","author":"yong Lee","year":"1986","journal-title":"SPE Reserv. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0450","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2118\/20383-PA","article-title":"A general history matching algorithm for three-phase, three-dimensional petroleum reservoirs","volume":"1","author":"Makhlouf","year":"1993","journal-title":"SPE Adv. Technol. Ser."},{"key":"10.1016\/j.asoc.2026.115222_bib0455","doi-asserted-by":"crossref","first-page":"056","DOI":"10.2118\/198913-PA","article-title":"Distributed Gauss-Newton optimization with smooth local parameterization for large-scale history-matching problems","volume":"25","author":"Xiao","year":"2020","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0460","first-page":"209","article-title":"A method for automatic history matching of a compositional reservoir simulator with multipoint flux approximation, comput","volume":"12","author":"Eydinov","year":"2008","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0465","series-title":"SPE Oil and Gas India Conference and Exhibition","article-title":"Reservoir model history matching with particle swarms","author":"Mohamed","year":"2010"},{"key":"10.1016\/j.asoc.2026.115222_bib0470","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/0004-3702(89)90050-7","article-title":"Classifier systems and genetic algorithms","volume":"40","author":"Booker","year":"1989","journal-title":"Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115222_bib0480","series-title":"SPE Reservoir Characterisation and Simulation Conference and Exhibition","article-title":"Field application study on automatic history matching using particle swarm optimization","author":"Lee","year":"2019"},{"key":"10.1016\/j.asoc.2026.115222_bib0485","doi-asserted-by":"crossref","DOI":"10.1016\/j.geothermics.2024.102958","article-title":"Optimizing geothermal reservoir modeling: a unified Bayesian PSO and BiGRU approach for precise history matching under uncertainty","volume":"119","author":"Ullah","year":"2024","journal-title":"Geothermics"},{"key":"10.1016\/j.asoc.2026.115222_bib0490","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s10596-016-9611-2","article-title":"Gaussian processes for history-matching: application to an unconventional gas reservoir","volume":"21","author":"Hamdi","year":"2017","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0495","doi-asserted-by":"crossref","first-page":"983","DOI":"10.2118\/203976-PA","article-title":"Robust method for reservoir simulation history matching using Bayesian inversion and long-short-term memory network-based proxy","volume":"28","author":"Zhang","year":"2023","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0500","doi-asserted-by":"crossref","first-page":"393","DOI":"10.2118\/117274-PA","article-title":"The Ensemble Kalman filter in reservoir engineering\u2014a review","volume":"14","author":"Aanonsen","year":"2009","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0505","series-title":"SPE Reservoir Simulation Symposium 2011","first-page":"1049","article-title":"An ensemble smoother for assisted history matching","volume":"vol. 2","author":"Skjervheim","year":"2011"},{"key":"10.1016\/j.asoc.2026.115222_bib0510","first-page":"16","article-title":"Application of an ensemble smoother with multiple data assimilation to the Bergermeer gas field, using PS-InSAR","volume":"5","author":"Fokker","year":"2016","journal-title":"Geomech. Energy Environ."},{"key":"10.1016\/j.asoc.2026.115222_bib0515","series-title":"Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2022","article-title":"Field study application of ensemble based assisted history matching and optimization for reservoir management","author":"Forouzanfar","year":"2022"},{"key":"10.1016\/j.asoc.2026.115222_bib0520","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.petlm.2019.07.004","article-title":"A multiobjective dominance and decomposition algorithm for reservoir model history matching","volume":"5","author":"Ilamah","year":"2019","journal-title":"Petroleum"},{"key":"10.1016\/j.asoc.2026.115222_bib0525","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.petrol.2016.01.038","article-title":"A hybrid differential evolution algorithm approach towards assisted history matching and uncertainty quantification for reservoir models","volume":"142","author":"Santhosh","year":"2016","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0530","author":"Frazier"},{"key":"10.1016\/j.asoc.2026.115222_bib0535","author":"Liu"},{"key":"10.1016\/j.asoc.2026.115222_bib0540","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S1876-3804(10)60021-3","article-title":"Geological features, major discoveries and unconventional petroleum Geology in the global petroleum exploration","volume":"37","author":"Caineng","year":"2010","journal-title":"Pet. Explor. Dev."},{"key":"10.1016\/j.asoc.2026.115222_bib0545","series-title":"SPE Russian Petroleum Technology Conference","article-title":"Geology driven history matching","author":"Matveev","year":"2019"},{"issue":"5","key":"10.1016\/j.asoc.2026.115222_bib0550","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1007\/s12145-024-01406-3","article-title":"Carbonate reservoir characterization and permeability modeling using machine learning \u2014 a study from Ras Fanar Field, Gulf of Suez, Egypt","volume":"17","author":"Khalid","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"10.1016\/j.asoc.2026.115222_bib0555","doi-asserted-by":"crossref","first-page":"657","DOI":"10.3390\/en18030657","article-title":"Boosting reservoir prediction accuracy: a hybrid methodology combining traditional reservoir simulation and modern machine learning approaches","volume":"18","author":"Otmane","year":"2025","journal-title":"Energies (Basel)."},{"key":"10.1016\/j.asoc.2026.115222_bib0560","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.109332","article-title":"End-to-end neural network approach to 3d reservoir simulation and adaptation","volume":"208","author":"Illarionov","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0565","article-title":"History matching of petroleum reservoirs using deep neural networks","volume":"16","author":"Alguliyev","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115222_bib0570","author":"Etienam"},{"key":"10.1016\/j.asoc.2026.115222_bib0575","doi-asserted-by":"crossref","first-page":"3450","DOI":"10.1016\/j.petsci.2023.10.019","article-title":"Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition","volume":"20","author":"Han","year":"2023","journal-title":"Pet. Sci."},{"key":"10.1016\/j.asoc.2026.115222_bib0580","doi-asserted-by":"crossref","DOI":"10.1016\/j.cnsns.2025.109551","article-title":"Emerging applications of physics-informed and physics-guided machine learning in geoenergy science: a review","volume":"154","author":"Davoodi","year":"2026","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"10.1016\/j.asoc.2026.115222_bib0585","doi-asserted-by":"crossref","first-page":"860","DOI":"10.3390\/en16020860","article-title":"Parallel automatic history matching algorithm using reinforcement learning","volume":"16","author":"Alolayan","year":"2023","journal-title":"Energies (Basel)."},{"key":"10.1016\/j.asoc.2026.115222_bib0590","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.petsci.2022.08.016","article-title":"Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty","volume":"20","author":"Wang","year":"2023","journal-title":"Pet. Sci."},{"key":"10.1016\/j.asoc.2026.115222_bib0595","article-title":"Neural operator-based proxy for reservoir simulations considering varying well settings, locations, and permeability fields, comput","volume":"196","author":"Badawi","year":"2025","journal-title":"Geosci."},{"key":"10.1016\/j.asoc.2026.115222_bib0600","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.2118\/219444-PA","article-title":"Field application of a novel multiresolution multiwell unconventional reservoir simulation: history matching and parameter identification","volume":"29","author":"Fu","year":"2024","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0605","article-title":"Feature-based ensemble history matching in a fractured carbonate reservoir using time-lapse deep electromagnetic tomography","volume":"208","author":"Zhang","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115222_bib0610","series-title":"SPE Norway Subsurface Conference","article-title":"Optimal well placement using machine learning methods: multiple reservoir scenarios","author":"Mousavi","year":"2020"},{"key":"10.1016\/j.asoc.2026.115222_bib0615","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2022.125125","article-title":"Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization","volume":"326","author":"Chen","year":"2022","journal-title":"Fuel"},{"key":"10.1016\/j.asoc.2026.115222_bib0620","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131618","article-title":"Enhancing interpretability of AI models in reservoir operation simulation: exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks","volume":"639","author":"Mahmoud","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.asoc.2026.115222_bib0625","doi-asserted-by":"crossref","first-page":"842","DOI":"10.3390\/en18040842","article-title":"Review of machine learning methods for steady state capacity and transient production forecasting in oil and gas reservoir","volume":"18","author":"Fan","year":"2025","journal-title":"Energies (Basel)."},{"key":"10.1016\/j.asoc.2026.115222_bib0630","series-title":"Proceedings of Machine Learning and Systems","first-page":"77","article-title":"Matchmaker: data drift mitigation in machine learning for large-scale systems","author":"Mallick","year":"2022"},{"key":"10.1016\/j.asoc.2026.115222_bib0635","article-title":"Full-field petroleum production forecasting: is deep learning all you need?","author":"Kubota","year":"2026","journal-title":"Pet. Res."},{"key":"10.1016\/j.asoc.2026.115222_bib0640","series-title":"Data Analytics in Reservoir Engineering","first-page":"65","article-title":"Future trends","author":"Sankaran","year":"2020"},{"key":"10.1016\/j.asoc.2026.115222_bib0645","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.2118\/187430-PA","article-title":"Enhancing the performance of the distributed Gauss-Newton optimization method by reducing the effect of numerical noise and truncation error with support-vector regression","volume":"23","author":"Guo","year":"2018","journal-title":"SPE J."},{"key":"10.1016\/j.asoc.2026.115222_bib0650","article-title":"Enhancing subsurface multiphase flow simulation with Fourier neural operator","volume":"10","author":"Ma","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.asoc.2026.115222_bib0655","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626006708?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626006708?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:57:45Z","timestamp":1780415865000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626006708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":124,"alternative-id":["S1568494626006708"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115222","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A review of artificial intelligence techniques applied to subsurface oil and gas reservoir simulation","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115222","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115222"}}