Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model
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| Published in: | Petroleum science Vol. 20; no. 5; pp. 2951 - 2966 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2023
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| ISSN: | 1995-8226 |
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| Author | Yang, Jiao-Sheng Song, Hong-Qing Du, Shu-Yi Zhao, Xiang-Guo Xie, Chi-Yu Zhu, Jing-Wei Wang, Jiu-Long |
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| Author_xml | – sequence: 1 givenname: Shu-Yi surname: Du fullname: Du, Shu-Yi – sequence: 2 givenname: Xiang-Guo surname: Zhao fullname: Zhao, Xiang-Guo – sequence: 3 givenname: Chi-Yu orcidid: 0000-0002-2813-6925 surname: Xie fullname: Xie, Chi-Yu – sequence: 4 givenname: Jing-Wei orcidid: 0000-0003-3669-5118 surname: Zhu fullname: Zhu, Jing-Wei – sequence: 5 givenname: Jiu-Long surname: Wang fullname: Wang, Jiu-Long – sequence: 6 givenname: Jiao-Sheng surname: Yang fullname: Yang, Jiao-Sheng – sequence: 7 givenname: Hong-Qing orcidid: 0000-0002-6642-3773 surname: Song fullname: Song, Hong-Qing |
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| Cites_doi | 10.25080/Majora-8b375195-003 10.1016/j.jngse.2016.08.045 10.1016/j.petsci.2021.07.001 10.2118/163613-PA 10.1016/j.fuel.2021.121872 10.1016/j.apenergy.2020.115695 10.1007/s10957-006-9101-0 10.1016/j.petrol.2018.06.075 10.1186/1471-2105-8-25 10.46690/ager.2021.03.06 10.2118/205013-PA 10.1016/j.petsci.2022.05.002 10.1016/j.apenergy.2022.118985 10.1007/s00158-009-0460-7 10.1016/j.energy.2019.01.087 10.1016/j.apenergy.2018.09.182 10.2118/9723-PA 10.2118/109805-PA 10.1016/S0167-9473(01)00065-2 10.1016/j.petrol.2022.110360 10.1109/TEVC.2004.826067 10.1016/j.petsci.2021.08.007 10.1109/TPAMI.2009.187 10.3390/pr5030034 10.1038/nature14541 10.1016/j.petsci.2022.02.008 10.2118/118926-PA 10.2118/201229-PA 10.2118/95322-PA 10.1080/00107510500052444 10.1023/B:STCO.0000035301.49549.88 10.1002/nme.5342 10.2118/199357-PA 10.2118/83381-PA 10.1198/tast.2009.08199 10.1023/A:1010933404324 10.1109/JIOT.2020.2992811 10.1016/j.jngse.2019.103038 10.1002/ese3.1276 10.1006/jcss.1997.1504 10.1155/2013/151898 10.1007/s11749-016-0481-7 10.1038/nature14539 10.1287/moor.6.1.19 10.1016/j.petrol.2018.03.044 10.2118/191378-PA 10.1016/j.fuel.2019.116758 10.2118/29124-PA 10.1016/j.gsf.2020.03.007 10.26804/ager.2020.02.03 |
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| References | Al-Mudhafar (10.1016/j.petsci.2023.04.001_bib6) 2017 Grömping (10.1016/j.petsci.2023.04.001_bib32) 2009; 63 Yousef (10.1016/j.petsci.2023.04.001_bib69) 2006; 9 Mohanta (10.1016/j.petsci.2023.04.001_bib46) 2021; 18 Chen (10.1016/j.petsci.2023.04.001_bib16) 2016 Coello (10.1016/j.petsci.2023.04.001_bib20) 2004; 8 Zhang (10.1016/j.petsci.2023.04.001_bib72) 2021; 12 Yu (10.1016/j.petsci.2023.04.001_bib70) 2013 Polikar (10.1016/j.petsci.2023.04.001_bib50) 2012 Rodriguez (10.1016/j.petsci.2023.04.001_bib51) 2009; 32 Al-Mudhafer (10.1016/j.petsci.2023.04.001_bib9) 2012 Ghassemzadeh (10.1016/j.petsci.2023.04.001_bib30) 2016; 35 Biau (10.1016/j.petsci.2023.04.001_bib13) 2016; 25 Hecht-Nielsen (10.1016/j.petsci.2023.04.001_bib34) 1992 Chen (10.1016/j.petsci.2023.04.001_bib17) 2010; 13 Wang (10.1016/j.petsci.2023.04.001_bib61) 2002 You (10.1016/j.petsci.2023.04.001_bib66) 2020; 279 Thanh (10.1016/j.petsci.2023.04.001_bib58) 2022; 314 Kaelo (10.1016/j.petsci.2023.04.001_bib39) 2006; 130 Breiman (10.1016/j.petsci.2023.04.001_bib14) 2001; 45 Fonseca (10.1016/j.petsci.2023.04.001_bib25) 2017; 109 Horn (10.1016/j.petsci.2023.04.001_bib37) 1994 Kennedy (10.1016/j.petsci.2023.04.001_bib40) 1995; 4 Du (10.1016/j.petsci.2023.04.001_bib23) 2022; 126121 Goodwin (10.1016/j.petsci.2023.04.001_bib31) 2015 Song (10.1016/j.petsci.2023.04.001_bib56) 2022; 212 Marler (10.1016/j.petsci.2023.04.001_bib44) 2010; 41 Al-Mudhafar (10.1016/j.petsci.2023.04.001_bib5) 2019; 72 You (10.1016/j.petsci.2023.04.001_bib67) 2020; 264 Solis (10.1016/j.petsci.2023.04.001_bib55) 1981; 6 Hong (10.1016/j.petsci.2023.04.001_bib36) 2022; 19 Fang (10.1016/j.petsci.2023.04.001_bib24) 1996; 11 Mofid (10.1016/j.petsci.2023.04.001_bib45) 2019; 172 Jansen (10.1016/j.petsci.2023.04.001_bib38) 2009 Drucker (10.1016/j.petsci.2023.04.001_bib22) 1997; 97 Liu (10.1016/j.petsci.2023.04.001_bib43) 2021; 18 Smola (10.1016/j.petsci.2023.04.001_bib53) 2004; 14 Guo (10.1016/j.petsci.2023.04.001_bib33) 2018; 23 Young (10.1016/j.petsci.2023.04.001_bib68) 1983; 23 Al-Mudhafar (10.1016/j.petsci.2023.04.001_bib8) 2022 Friedman (10.1016/j.petsci.2023.04.001_bib28) 2001 Patil (10.1016/j.petsci.2023.04.001_bib49) 2014; 107 Li (10.1016/j.petsci.2023.04.001_bib42) 2018; 232 Yin (10.1016/j.petsci.2023.04.001_bib65) 2021; 26 Snoek (10.1016/j.petsci.2023.04.001_bib54) 2012; 25 Bhattacharya (10.1016/j.petsci.2023.04.001_bib12) 2018; 170 Ghahramani (10.1016/j.petsci.2023.04.001_bib29) 2015; 521 Zubarev (10.1016/j.petsci.2023.04.001_bib75) 2009 Freund (10.1016/j.petsci.2023.04.001_bib26) 1997; 55 Ahmad (10.1016/j.petsci.2023.04.001_bib2) 2016 Strobl (10.1016/j.petsci.2023.04.001_bib57) 2007; 8 Friedman (10.1016/j.petsci.2023.04.001_bib27) 2002; 38 Xue (10.1016/j.petsci.2023.04.001_bib63) 2020 Coats (10.1016/j.petsci.2023.04.001_bib19) 1995 Chen (10.1016/j.petsci.2023.04.001_bib15) 2020; 25 Deb (10.1016/j.petsci.2023.04.001_bib21) 2014 Odeh (10.1016/j.petsci.2023.04.001_bib48) 1981; 33 Zhang (10.1016/j.petsci.2023.04.001_bib71) 2022; 19 Chertenkov (10.1016/j.petsci.2023.04.001_bib18) 2012 LeCun (10.1016/j.petsci.2023.04.001_bib41) 2015; 521 Bergstra (10.1016/j.petsci.2023.04.001_bib11) 2013 Salam (10.1016/j.petsci.2023.04.001_bib52) 2015 Wang (10.1016/j.petsci.2023.04.001_bib60) 2009; 14 Zhao (10.1016/j.petsci.2023.04.001_bib73) 2020; 25 Zhu (10.1016/j.petsci.2023.04.001_bib74) 2020; 4 Wang (10.1016/j.petsci.2023.04.001_bib62) 2020; 7 Yan (10.1016/j.petsci.2023.04.001_bib64) 2014; 19 Albertoni (10.1016/j.petsci.2023.04.001_bib3) 2003; 6 Ho (10.1016/j.petsci.2023.04.001_bib35) 1995 Alwated (10.1016/j.petsci.2023.04.001_bib10) 2021; 5 Newman (10.1016/j.petsci.2023.04.001_bib47) 2005; 46 Udy (10.1016/j.petsci.2023.04.001_bib59) 2017; 5 Abad (10.1016/j.petsci.2023.04.001_bib1) 2022; 308 Al-Mudhafar (10.1016/j.petsci.2023.04.001_bib4) 2016 Al-Mudhafar (10.1016/j.petsci.2023.04.001_bib7) 2018; 166 |
| References_xml | – year: 2013 ident: 10.1016/j.petsci.2023.04.001_bib11 article-title: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms doi: 10.25080/Majora-8b375195-003 – volume: 35 start-page: 89 year: 2016 ident: 10.1016/j.petsci.2023.04.001_bib30 article-title: Optimization of integrated production system using advanced proxy based models: a new approach publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2016.08.045 – year: 2012 ident: 10.1016/j.petsci.2023.04.001_bib9 article-title: Application of a hybrid system of genetic algorithm & fuzzy logic as optimization techniques for improving oil recovery in a sandstone reservoirs in Iraq – volume: 18 start-page: 1230 issue: 4 year: 2021 ident: 10.1016/j.petsci.2023.04.001_bib46 article-title: Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit publication-title: Petrol. Sci. doi: 10.1016/j.petsci.2021.07.001 – start-page: 1 year: 2012 ident: 10.1016/j.petsci.2023.04.001_bib50 article-title: Ensemble learning – volume: 19 start-page: 873 issue: 5 year: 2014 ident: 10.1016/j.petsci.2023.04.001_bib64 article-title: Optimization algorithms based on combining FD approximations and stochastic gradients compared with methods based only on a stochastic gradient publication-title: SPE J. doi: 10.2118/163613-PA – volume: 308 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib1 article-title: Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields publication-title: Fuel doi: 10.1016/j.fuel.2021.121872 – year: 2002 ident: 10.1016/j.petsci.2023.04.001_bib61 article-title: Optimization of production operations in petroleum fields – volume: 279 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib66 article-title: Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.115695 – volume: 130 start-page: 253 issue: 2 year: 2006 ident: 10.1016/j.petsci.2023.04.001_bib39 article-title: Some variants of the controlled random search algorithm for global optimization publication-title: J. Optim. Theor. Appl. doi: 10.1007/s10957-006-9101-0 – year: 2012 ident: 10.1016/j.petsci.2023.04.001_bib18 article-title: Gas breakthrough detection and production monitoring from ICD screen completion on Lukoil's Korchagina Field using permanently installed distributed temperature sensors – start-page: 403 year: 2014 ident: 10.1016/j.petsci.2023.04.001_bib21 – volume: 25 year: 2012 ident: 10.1016/j.petsci.2023.04.001_bib54 article-title: Practical bayesian optimization of machine learning algorithms publication-title: Adv. Neural Inf. Process. Syst. – year: 2017 ident: 10.1016/j.petsci.2023.04.001_bib6 article-title: Proxy-based metamodeling optimization of the gas-assisted gravity drainage GAGD process in heterogeneous sandstone reservoirs – volume: 170 start-page: 1005 year: 2018 ident: 10.1016/j.petsci.2023.04.001_bib12 article-title: Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: case studies from the Appalachian basin, USA publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2018.06.075 – volume: 8 start-page: 1 issue: 1 year: 2007 ident: 10.1016/j.petsci.2023.04.001_bib57 article-title: Bias in random forest variable importance measures: illustrations, sources and a solution publication-title: BMC Bioinf. doi: 10.1186/1471-2105-8-25 – start-page: 1189 year: 2001 ident: 10.1016/j.petsci.2023.04.001_bib28 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Stat. – volume: 5 start-page: 297 issue: 3 year: 2021 ident: 10.1016/j.petsci.2023.04.001_bib10 article-title: Enhanced oil recovery by nanoparticles flooding: from numerical modeling improvement to machine learning prediction publication-title: Advances in Geo-Energy Research doi: 10.46690/ager.2021.03.06 – volume: 26 start-page: 1614 issue: 4 year: 2021 ident: 10.1016/j.petsci.2023.04.001_bib65 article-title: Multifidelity genetic transfer: an efficient framework for production optimization publication-title: SPE J. doi: 10.2118/205013-PA – volume: 19 start-page: 3004 issue: 6 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib36 article-title: A liquid loading prediction method of gas pipeline based on machine learning publication-title: Petrol. Sci. doi: 10.1016/j.petsci.2022.05.002 – year: 1994 ident: 10.1016/j.petsci.2023.04.001_bib37 article-title: A niched Pareto genetic algorithm for multiobjective optimization – volume: 314 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib58 article-title: Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers publication-title: Appl. Energy doi: 10.1016/j.apenergy.2022.118985 – year: 2016 ident: 10.1016/j.petsci.2023.04.001_bib16 article-title: Xgboost: a scalable tree boosting system – volume: 41 start-page: 853 issue: 6 year: 2010 ident: 10.1016/j.petsci.2023.04.001_bib44 article-title: The weighted sum method for multi-objective optimization: new insights publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-009-0460-7 – year: 2016 ident: 10.1016/j.petsci.2023.04.001_bib4 article-title: Incorporation of bootstrapping and cross-validation for efficient multivariate facies and Petrophysical modeling – volume: 172 start-page: 286 year: 2019 ident: 10.1016/j.petsci.2023.04.001_bib45 article-title: Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm publication-title: Energy doi: 10.1016/j.energy.2019.01.087 – volume: 232 start-page: 197 year: 2018 ident: 10.1016/j.petsci.2023.04.001_bib42 article-title: Random forest regression for online capacity estimation of lithium-ion batteries publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.09.182 – volume: 33 start-page: 13 issue: 1 year: 1981 ident: 10.1016/j.petsci.2023.04.001_bib48 article-title: Comparison of solutions to a three-dimensional black-oil reservoir simulation problem (includes associated paper 9741) publication-title: J. Petrol. Technol. doi: 10.2118/9723-PA – volume: 14 start-page: 506 issue: 3 year: 2009 ident: 10.1016/j.petsci.2023.04.001_bib60 article-title: Production optimization in closed-loop reservoir management publication-title: SPE J. doi: 10.2118/109805-PA – volume: 38 start-page: 367 issue: 4 year: 2002 ident: 10.1016/j.petsci.2023.04.001_bib27 article-title: Stochastic gradient boosting publication-title: Comput. Stat. Data Anal. doi: 10.1016/S0167-9473(01)00065-2 – volume: 212 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib56 article-title: Evaluation of hydraulic fracturing effect on coalbed methane reservoir based on deep learning method considering physical constraints publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2022.110360 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 10.1016/j.petsci.2023.04.001_bib20 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.826067 – volume: 18 start-page: 1450 issue: 5 year: 2021 ident: 10.1016/j.petsci.2023.04.001_bib43 article-title: A deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells publication-title: Petrol. Sci. doi: 10.1016/j.petsci.2021.08.007 – volume: 126121 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib23 article-title: A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns publication-title: Energy – year: 1995 ident: 10.1016/j.petsci.2023.04.001_bib35 article-title: Random decision forests – volume: 32 start-page: 569 issue: 3 year: 2009 ident: 10.1016/j.petsci.2023.04.001_bib51 article-title: Sensitivity analysis of k-fold cross validation in prediction error estimation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2009.187 – volume: 5 start-page: 34 issue: 3 year: 2017 ident: 10.1016/j.petsci.2023.04.001_bib59 article-title: Review of field development optimization of waterflooding, EOR, and well placement focusing on history matching and optimization algorithms publication-title: Processes doi: 10.3390/pr5030034 – volume: 521 start-page: 452 issue: 7553 year: 2015 ident: 10.1016/j.petsci.2023.04.001_bib29 article-title: Probabilistic machine learning and artificial intelligence publication-title: Nature doi: 10.1038/nature14541 – volume: 19 start-page: 1566 issue: 4 year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib71 article-title: Predicting gas-bearing distribution using DNN based on multi-component seismic data: quality evaluation using structural and fracture factors publication-title: Petrol. Sci. doi: 10.1016/j.petsci.2022.02.008 – volume: 13 start-page: 56 issue: 1 year: 2010 ident: 10.1016/j.petsci.2023.04.001_bib17 article-title: Ensemble-based closed-loop optimization applied to Brugge field publication-title: SPE Reservoir Eval. Eng. doi: 10.2118/118926-PA – volume: 25 start-page: 2450 issue: 5 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib73 article-title: A classification-based surrogate-assisted multiobjective evolutionary algorithm for production optimization under geological uncertainty publication-title: SPE J. doi: 10.2118/201229-PA – volume: 23 start-page: 727 issue: 5 year: 1983 ident: 10.1016/j.petsci.2023.04.001_bib68 article-title: A generalized compositional approach for reservoir simulation publication-title: SPE J. – volume: 9 start-page: 630 issue: 6 year: 2006 ident: 10.1016/j.petsci.2023.04.001_bib69 article-title: A capacitance model to infer interwell connectivity from production and injection rate fluctuations publication-title: SPE Reservoir Eval. Eng. doi: 10.2118/95322-PA – year: 2009 ident: 10.1016/j.petsci.2023.04.001_bib38 article-title: Closed loop reservoir management publication-title: SPE Reservoir Simulation Symposium – volume: 46 start-page: 323 issue: 5 year: 2005 ident: 10.1016/j.petsci.2023.04.001_bib47 article-title: Power laws, Pareto distributions and Zipf's law publication-title: Contemp. Phys. doi: 10.1080/00107510500052444 – year: 2015 ident: 10.1016/j.petsci.2023.04.001_bib52 article-title: Production optimization strategy using hybrid genetic algorithm – volume: 14 start-page: 199 issue: 3 year: 2004 ident: 10.1016/j.petsci.2023.04.001_bib53 article-title: A tutorial on support vector regression publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 – volume: 109 start-page: 1756 issue: 13 year: 2017 ident: 10.1016/j.petsci.2023.04.001_bib25 article-title: A stochastic simplex approximate gradient (StoSAG) for optimization under uncertainty publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.5342 – year: 2016 ident: 10.1016/j.petsci.2023.04.001_bib2 article-title: Rejuvenating a high GOR, light oil reservoir using AICD completion technology for gas control – volume: 4 start-page: 1942 year: 1995 ident: 10.1016/j.petsci.2023.04.001_bib40 article-title: Particle swarm optimization – volume: 25 start-page: 105 issue: 1 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib15 article-title: Global and local surrogate-model-assisted differential evolution for waterflooding production optimization publication-title: SPE J. doi: 10.2118/199357-PA – volume: 97 start-page: 107 year: 1997 ident: 10.1016/j.petsci.2023.04.001_bib22 article-title: Improving regressors using boosting techniques publication-title: ICML – volume: 6 start-page: 6 issue: 1 year: 2003 ident: 10.1016/j.petsci.2023.04.001_bib3 article-title: Inferring interwell connectivity only from well-rate fluctuations in waterfloods publication-title: SPE Reservoir Eval. Eng. doi: 10.2118/83381-PA – volume: 63 start-page: 308 issue: 4 year: 2009 ident: 10.1016/j.petsci.2023.04.001_bib32 article-title: Variable importance assessment in regression: linear regression versus random forest publication-title: Am. Statistician doi: 10.1198/tast.2009.08199 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.petsci.2023.04.001_bib14 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – start-page: 65 year: 1992 ident: 10.1016/j.petsci.2023.04.001_bib34 article-title: Theory of the backpropagation neural network.(s) – volume: 7 start-page: 8641 issue: 9 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib62 article-title: Random forest-bayesian optimization for product quality prediction with large-scale dimensions in process industrial cyber–physical systems publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2992811 – volume: 72 year: 2019 ident: 10.1016/j.petsci.2023.04.001_bib5 article-title: Polynomial and nonparametric regressions for efficient predictive proxy metamodeling: application through the CO2-EOR in shale oil reservoirs publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2019.103038 – year: 2022 ident: 10.1016/j.petsci.2023.04.001_bib8 article-title: Rapid evaluation and optimization of carbon dioxide-enhanced oil recovery using reduced-physics proxy models publication-title: Energy Science & Engineering doi: 10.1002/ese3.1276 – volume: 55 start-page: 119 issue: 1 year: 1997 ident: 10.1016/j.petsci.2023.04.001_bib26 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J. Comput. Syst. Sci. doi: 10.1006/jcss.1997.1504 – year: 2013 ident: 10.1016/j.petsci.2023.04.001_bib70 article-title: Optimization of multiple hydraulically fractured horizontal wells in unconventional gas reservoirs publication-title: J. Petroleum Eng. doi: 10.1155/2013/151898 – volume: 25 start-page: 197 issue: 2 year: 2016 ident: 10.1016/j.petsci.2023.04.001_bib13 article-title: A random forest guided tour publication-title: Test doi: 10.1007/s11749-016-0481-7 – year: 1995 ident: 10.1016/j.petsci.2023.04.001_bib19 article-title: Compositional and black oil reservoir simulation – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.petsci.2023.04.001_bib41 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 107 issue: 4 year: 2014 ident: 10.1016/j.petsci.2023.04.001_bib49 article-title: Multi-objective particle swarm optimization (MOPSO) based on Pareto dominance approach publication-title: Int. J. Comput. Appl. – volume: 6 start-page: 19 issue: 1 year: 1981 ident: 10.1016/j.petsci.2023.04.001_bib55 article-title: Minimization by random search techniques publication-title: Math. Oper. Res. doi: 10.1287/moor.6.1.19 – volume: 166 start-page: 490 year: 2018 ident: 10.1016/j.petsci.2023.04.001_bib7 article-title: Robust optimization of cyclic CO2 flooding through the gas-assisted gravity drainage process under geological uncertainties publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2018.03.044 – volume: 23 start-page: 2409 issue: 6 year: 2018 ident: 10.1016/j.petsci.2023.04.001_bib33 article-title: Robust life-cycle production optimization with a support-vector-regression proxy publication-title: SPE J. doi: 10.2118/191378-PA – volume: 264 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib67 article-title: Development and application of a machine learning based multi-objective optimization workflow for CO2-EOR projects publication-title: Fuel doi: 10.1016/j.fuel.2019.116758 – year: 2009 ident: 10.1016/j.petsci.2023.04.001_bib75 article-title: Pros and cons of applying proxy-models as a substitute for full reservoir simulations – volume: 11 start-page: 116 issue: 2 year: 1996 ident: 10.1016/j.petsci.2023.04.001_bib24 article-title: A generalized well-management scheme for reservoir simulation publication-title: SPE Reservoir Eng. doi: 10.2118/29124-PA – year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib63 article-title: Affine transformation-enhanced multifactorial optimization for heterogeneous problems publication-title: IEEE Trans. Cybern. – year: 2015 ident: 10.1016/j.petsci.2023.04.001_bib31 article-title: Bridging the gap between deterministic and probabilistic uncertainty quantification using advanced proxy based methods – volume: 12 start-page: 469 issue: 1 year: 2021 ident: 10.1016/j.petsci.2023.04.001_bib72 article-title: Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization publication-title: Geosci. Front. doi: 10.1016/j.gsf.2020.03.007 – volume: 4 start-page: 135 issue: 2 year: 2020 ident: 10.1016/j.petsci.2023.04.001_bib74 article-title: High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model publication-title: Advances in Geo-Energy Research doi: 10.26804/ager.2020.02.03 |
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