石油学报 ›› 2023, Vol. 44 ›› Issue (9): 1574-1586.DOI: 10.7623/syxb202309014
人工智能在注水开发方案精细化调整中的应用现状及展望
刘合1,2, 李艳春1, 贾德利2, 王素玲1, 乔美霞1, 屈如意1, 温鹏云1, 任智慧1
- 1. 东北石油大学机械科学与工程学院 黑龙江大庆 163318;
2. 中国石油勘探开发研究院 北京 100083
-
收稿日期:2023-03-30修回日期:2023-07-09出版日期:2023-09-25发布日期:2023-10-09 -
通讯作者:李艳春,女,1994年4月生,2017年获东北石油大学硕士学位,现为东北石油大学博士研究生,主要从事人工智能在油藏开发中的应用研究。Email:Yachne_Li@163.com -
作者简介:刘合,男,1961年3月生,2002年获哈尔滨工程大学博士学位,现为中国石油勘探开发研究院副总工程师、中国工程院院士,主要从事低渗透油气藏增产改造、机采系统提高系统效率、分层注水和井筒工程控制技术等方面的研究。Email:liuhe@petrochina.com.cn -
基金资助:国家自然科学基金面上项目"大数据驱动下的老油田水驱精细分析方法"(No.52074345)和国家自然科学基金科学中心项目"数字经济时代的资源环境管理理论与应用"(No.72088101)资助。
Application status and prospects of artificial intelligence in the refinement of waterflooding development program
Liu He1,2, Li Yanchun1, Jia Deli2, Wang Suling1, Qiao Meixia1, Qu Ruyi1, Wen Pengyun1, Ren Zhihui1
- 1. School of Mechanical Science and Engineering, Northeast Petroleum University, Heilongjiang Daqing 163318, China;
2. PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
-
Received:2023-03-30Revised:2023-07-09Online:2023-09-25Published:2023-10-09
摘要： 水驱开发油田由于注采关系复杂、驱替场动态变化频繁以及长期注水,导致层间矛盾加剧,已进入到深度精细注水开发的新阶段。结合静态与动态生产数据进行注水开发方案调整,有利于掌握油藏的动态变化与实现有效剩余油挖潜。为保证优化的注水方案和先进的分注工艺相结合,系统综述了油藏动态分析技术发展现状,重点阐述了人工智能方法与油藏工程交叉融合辅助注水开发方案调整的核心问题,同时结合前沿智能化理论与方法对未来注水开发方案智能精细化调整趋势进行了探讨和展望,即充分利用精细智能分层注水工艺实时监测的大量动态生产数据。未来注水开发方案优化的研究重点将聚焦于"动态数据+物理约束+人工智能算法"的深度融合,进一步推动水驱开发油田监测数据实时采集、油藏动态实时预测和注水方案实时优化的智能优化应用落地,最终实现注水方案设计与优化和井下分层注水实时调整同步的油藏和采油工程一体化。
中图分类号:
引用本文
刘合, 李艳春, 贾德利, 王素玲, 乔美霞, 屈如意, 温鹏云, 任智慧. 人工智能在注水开发方案精细化调整中的应用现状及展望[J]. 石油学报, 2023, 44(9): 1574-1586.
Liu He, Li Yanchun, Jia Deli, Wang Suling, Qiao Meixia, Qu Ruyi, Wen Pengyun, Ren Zhihui. Application status and prospects of artificial intelligence in the refinement of waterflooding development program[J]. Acta Petrolei Sinica, 2023, 44(9): 1574-1586.
| [1] 裘亦楠.中国陆相碎屑岩储层沉积学的进展[J].沉积学报,1992,10(3):16-24. QIU Yinan.Developments in reservoir sedimentology of continental clastic rocks in China[J].Acta Sedimentologica Sinica,1992,10(3):16-24. [2] 朱丽红,杜庆龙,姜雪岩,等.陆相多层砂岩油藏特高含水期三大矛盾特征及对策[J].石油学报,2015,36(2):210-216. ZHU Lihong,DU Qinglong,JIANG Xueyan,et al.Characteristics and strategies of three major contradictions for continental facies multi-layered sandstone reservoir at ultra-high water cut stage[J].Acta Petrolei Sinica,2015,36(2):210-216. [3] 刘合,郑立臣,杨清海,等.分层采油技术的发展历程和展望[J].石油勘探与开发,2020,47(5):1027-1038. LIU He,ZHENG Lichen,YANG Qinghai,et al.Development and prospect of separated zone oil production technology[J].Petroleum Exploration and Development,2020,47(5):1027-1038. [4] 刘合,裴晓含,贾德利,等.第四代分层注水技术内涵、应用与展望[J].石油勘探与开发,2017,44(4):608-614. LIU He,PEI Xiaohan,JIA Deli,et al.Connotation,application and prospect of the fourth-generation separated layer water injection technology[J].Petroleum Exploration and Development,2017,44(4):608-614. [5] 刘合,肖国华,孙福超,等.新型大斜度井同心分层注水技术[J].石油勘探与开发,2015,42(4):512-517. LIU He,XIAO Guohua,SUN Fuchao,et al.A new concentric zonal water injection technique for highly-deviated wells[J].Petroleum Exploration and Development,2015,42(4):512-517. [6] 苏健,刘合.石油工程大数据应用的挑战与发展[J].中国石油大学学报:社会科学版,2020,36(3):1-6. SU Jian,LIU He.Challenge and development of big data application in petroleum engineering[J].Journal of China University of Petroleum:Edition of Social Sciences,2020,36(3):1-6. [7] 张凯,赵兴刚,张黎明,等.智能油田开发中的大数据及智能优化理论和方法研究现状及展望[J].中国石油大学学报:自然科学版,2020,44(4):28-38. ZHANG Kai,ZHAO Xinggang,ZHANG Liming,et al.Current status and prospect for the research and application of big data and intelligent optimization methods in oilfield development[J].Journal of China University of Petroleum:Edition of Natural Science,2020,44(4):28-38. [8] 郑新权,师俊峰,曹刚,等.采油采气工程技术新进展与展望[J].石油勘探与开发,2022,49(3):565-576. ZHENG Xinquan,SHI Junfeng,CAO Gang,et al.Progress and prospects of oil and gas production engineering technology in China[J].Petroleum Exploration and Development,2022,49(3):565-576. [9] 宋来明,王春秋,卢川,等.数据驱动的复杂油藏注采生产优化技术研究进展[J].石油钻采工艺,2022,44(2):253-260. SONG Laiming,WANG Chunqiu,LU Chuan,et al.Research progress of data-driven injection production optimization of complex oil reservoirs[J].Oil Drilling & Production Technology,2022,44(2):253-260. [10] 匡立春,刘合,任义丽,等.人工智能在石油勘探开发领域的应用现状与发展趋势[J].石油勘探与开发,2021,48(1):1-11. KUANG Lichun,LIU He,REN Yili,et al.Application and development trend of artificial intelligence in petroleum exploration and development[J].Petroleum Exploration and Development,2021,48(1):1-11. [11] 杜庆龙.长期注水开发砂岩油田储层渗透率变化规律及微观机理[J].石油学报,2016,37(9):1159-1164. DU Qinglong.Variation law and microscopic mechanism of permeability in sandstone reservoir during long-term water flooding development[J].Acta Petrolei Sinica,2016,37(9):1159-1164. [12] MCKINLEY R M,VELA S,CARLTON L A.A field application of pulse-testing for detailed reservoir description[J].Journal of Petroleum Technology,1968,20(3):313-321. [13] JOURNEL A G.Geostatistics for conditional simulation of ore bodies[J].Economic Geology,1974,69(5):673-687. [14] 李乐中,张立.斯伦贝谢公司解释系统及软件介绍[J].国外测井技术,1992,7(1):111-113. Li Lezhong,Zhang Li.Introduction to interpretation system and software of schlumberger[J].World Well Logging Technology,1992,7(1):111-113. [15] HEARN C L,EBANKS W J JR,TYE R S,et al.Geological factors influencing reservoir performance of the Hartzog Draw Field,Wyoming[J].Journal of Petroleum Technology,1984,36(8):1335-1344. [16] HALDORSEN H H,DAMSLETH E.Stochastic modeling[J].Journal of Petroleum Technology,1990,42(4):404-412. [17] 金振武,钟兴水.测井解释技术的新进展——一种利用测井资料进行油藏描述的新技术[J].大庆石油地质与开发,1986,5(2):75-80. JIN Zhenwu,ZHONG Xingshui.Advances in log interpretation technique a new technique for reservoir description using log data[J].Petroleum Geology & Oilfield Development in Daqing,1986,5(2):75-80. [18] 纪发华,熊琦华,张一伟,等.地质统计学在油藏描述中的应用[M].东营:石油大学出版社,1992:1-7. JI Fahua,XIONG Qihua,ZHANG Yiwei,et al.Application of geostatistics to reservoir description[M].Dongying:Petroleum University Press,1992:1-7. [19] 潘和平.油藏描述数据库及其在油藏描述中的应用[J].石油勘探与开发,1992,19(1):101-107. PAN Heping.Reservoir descripition database used in jianghan oil filed and its application[J].Petroleum Expoloration and Development,1992,19(1):101-107. [20] 廖红伟,王凤琴,薛中天,等.基于大系统方法的油藏动态分析[J].石油学报,2002,23(6):45-49. LIAO Hongwei,WANG Fengqin,XUE Zhongtian,et al.Large scale system method in reservior performance[J].Acta Petrolei Sinica,2002,23(6):45-49. [21] 管纪昂,袁士义.油田开发动态指标预测方法[J].断块油气田,2005,12(4):37-38. GUAN Ji'ang,YUAN Shiyi.Dynamic index forecast methods of oilfield development[J].Fault-Block Oil & Gas Field,2005,12(4):37-38. [22] SKEIE G M,JOHANSEN F W,ØSTBY C,et al.A web based system for regional oil spill contingency and emergency response planning[R].SPE 61270,2000. [23] 刘合,裴晓含,罗凯,等.中国油气田开发分层注水工艺技术现状与发展趋势[J].石油勘探与开发,2013,40(6):733-737. LIU He,PEI Xiaohan,LUO Kai,et al.Current status and trend of separated layer water flooding in China[J].Petroleum Exploration and Development,2013,40(6):733-737. [24] 计秉玉.对油气藏工程研究方法发展趋势的几点认识[J].石油学报,2020,41(12):1774-1778. Ji Bingyu.Some understandings on the development trend in research of oil and gas reservoir engineering methods[J].Acta Petrolei Sinica,2020,41(12):1774-1778. [25] 王俊魁,王春瑞,方亮.采油井合理井底压力界限的确定方法[J].大庆石油地质与开发,1999,18(5):21-22. WANG Junkui,WANG Chunrui,FANG Liang.A method for determining of reasonable bottom hole pressure of production wells[J].Petroleum Geology & Oilfield Development in Daqing,1999,18(5):21-22. [26] 王志军,刘秀航,董静,等.高含水后期油田区块注采适应性定量评价方法及调整对策[J].大庆石油地质与开发,2005,24(6):51-53. WANG Zhijun,LIU Xiuhang,DONG Jing,et al.Quantitative evaluation method for injection-production adaptability of oil field in later high water cut stage and adjustment solution[J].Petroleum Geology & Oilfield Development in Daqing,2005,24(6):51-53. [27] 唐亮,殷艳玲,张贵才.注采系统连通性研究[J].石油天然气学报,2008,30(4):134-136. TANG Liang,YIN Yanling,ZHANG Guicai.Study on connectivity of an injection-production system[J].Journal of Oil and Gas Technology,2008,30(4):134-136. [28] GOMEZ V,GOMEZ A,DURAN J.Analytical simulation of the injection/production system of La Cira east and north areas using CGM method[R].SPE 121854,2009. [29] 张锐.油田注水开发效果评价方法[M].北京:石油工业出版社,2010. ZHANG Rui.Evaluation methods of the effect of water injection development in oilfield[M].Beijing:Petroleum Industry Press,2010. [30] 陈汶滨,赵明,蔡明俊,等.基于指标特征模型的油藏注水开发效果定量化评价[J].石油学报,2016,7(S2):80-86. CHEN Wenbin,ZHAO Ming,CAI Mingjun,et al.Quantitative evaluation of reservoir water-flooding development effect based on index characteristic model[J].Acta Petrolei Sinica,2016,37(S2):80-86. [31] 杨勇.胜利油田勘探开发大数据及人工智能技术应用进展[J].油气地质与采收率,2022,29(1):1-10. YANG Yong.Application progress of big data & AI technologies in exploration and development of Shengli oilfield[J].Petroleum Geology and Recovery Efficiency,2022,29(1):1-10. [32] 郑爱维,梁榜,舒志国,等.基于大数据PLS法的页岩气产能影响因素分析——以四川盆地涪陵气田焦石坝区块为例[J].天然气地球科学,2020,31(4):542-551. ZHENG Aiwei,LIANG Bang,SHU Zhiguo,et al.Analysis of influencing factors of shale gas productivity based on large data technology:a case of Jiaoshiba block in Fuling gas field,Sichuan Basin[J].Natural Gas Geoscience,2020,31(4):542-551. [33] GUPTA I,SAMANDARLI O,BURKS A,et al.Autoregressive and machine learning driven production forecasting-Midland Basin case study[R].URTEC-2021-5184,2021. [34] BALASHOV D,EGOROV D,BELOZEROV B,et al.Prediction of wells productive characteristics with the use of unsupervised machine learning algorithms[R].SPE 196857,2019. [35] KUBOTA L,REINERT D.Machine learning forecasts oil rate in mature onshore field jointly driven by water and steam injection[R].SPE 196152,2019. [36] ZHANG Liang,LI Zhiping,LAI Fengpeng,et al.Integrated optimization design for horizontal well placement and fracturing in tight oil reservoirs[J].Journal of Petroleum Science and Engineering,2019,178:82-96. [37] 钟仪华,王淑宁,罗兰,等.用深度学习挖掘油田开发指标预测模型的知识[J].西南石油大学学报:自然科学版,2020,42(6):63-74. ZHONG Yihua,WANG Shuning,LUO Lan,et al.Knowledge mining for oilfield development index prediction model using deep learning[J].Journal of Southwest Petroleum University:Science & Technology Edition,2020,42(6):63-74. [38] OLIVER D S,CHEN Yan.Recent progress on reservoir history matching:a review[J].Computational Geosciences,2011,15(1):185-221. [39] SENGEL A,TURKARSLAN G.Assisted history matching of a highly heterogeneous carbonate reservoir using hydraulic flow units and artificial neural networks[R].SPE 200541,2020. [40] LEE S,STEPHEN K.Field application study on automatic history matching using particle swarm optimization[R].SPE 196678,2019. [41] CANCHUMUNI S A,EMERICK A A,PACHECO M A,et al.Integration of ensemble data assimilation and deep learning for history matching facies models[R].OTC 28015,2017. [42] ZHANG Kai,YU Haiqun,MA Xiaopeng,et al.Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies[J].Petroleum Science,2022,19(2):707-719. [43] 贾德利,刘合,张吉群,等.大数据驱动下的老油田精细注水优化方法[J].石油勘探与开发.2020,47(3):629-636. JIA Deli,LIU He,ZHANG Jiqun,et al.Data-driven optimization for fine water injection in a mature oil field[J].Petroleum Exploration and Development,2020,47(3):629-636. [44] WONG L J,AMINI H,MACBETH C.Multi-dimensional approach to inter-well connectivity analysis[R].SPE 200563,2020. [45] HEFFER K J,FOX R J,MCGILL C A,et al.Novel techniques show links between reservoir flow directionality,earth stress,fault structure and geomechanical changes in mature waterfloods[J].SPE Journal,1997,2(2):91-98. [46] ALBERTONI A,LAKE L W.Inferring interwell connectivity only from well-rate fluctuations in waterfloods[J].SPE Reservoir Evaluation & Engineering,2003,6(1):6-16. [47] YOUSEF A A,GENTIL P,JENSEN J L,et al.A capacitance model to infer interwell connectivity from production-and injection-rate fluctuations[J].SPE Reservoir Evaluation & Engineering,2006,9(6):630-646. [48] KAVIANI D,VALKÓ P P,JENSEN J L.Application of the multiwell productivity index-based method to evaluate interwell connectivity[R].SPE 129965,2010. [49] LIU Feilong,MENDEL J M,NEJAD A M.Forecasting injector/producer relationships from production and injection rates using an extended Kalman filter[J].SPE Journal,2009,14(4):653-664. [50] DEMIRYUREK U,BANAEI-KASHANI F,SHAHABI C,et al.Neural-network based sensitivity analysis for injector-producer relationship identification[R].SPE 112124,2008. [51] LIU Wei,LIU W D,GU Jianwei.Reservoir inter-well connectivity analysis based on a data driven method[R].SPE 197654,2019. [52] 尚福华,郑伟.基于决策树的注采连通关系判别研究[J].计算机应用研究,2013,30(7):2051-2054. SHANG Fuhua,ZHENG Wei.Study on inferring interwell connectivity of injection-production system based on decision tree[J].Application Research of Computers,2013,30(7):2051-2054. [53] 赵辉,康志江,孙海涛,等.水驱开发多层油藏井间连通性反演模型[J].石油勘探与开发,2016,43(1):99-106. ZHAO Hui,KANG Zhijiang,SUN Haitao,et al.An interwell connectivity inversion model for waterflooded multilayer reservoirs[J].Petroleum Exploration and Development,2016,43(1):99-106. [54] 赵艳红,姜汉桥,李洪奇.注水开发油田注水通道状态辨识及预测方法[J].石油学报,2021,42(8):1081-1090. ZHAO Yanhong,JIANG Hanqiao,LI Hongqi.Identification and predictions of water injectivity for water injection channels in water injection development oilfield[J].Acta Petrolei Sinica,2021,42(8):1081-1090. [55] ZHANG Jiqun,LI Hua,LI Xinhao,et al.Fine evaluation method of dynamic water-flooding reserves control degree of mature oilfields[C]//Proceedings of 2019 IEEE International Conference on Artificial Intelligence and Computer Applications.Dalian:IEEE,2019. [56] 刘德华,李士伦,吴军.矢量化井网的概念及布井方法初探[J].江汉石油学院学报,2004,26(4):110-111. LIU Dehua,LI Shilun,WU Jun.Concept of vector well pattern and method of well pattern arrangement[J].Journal of Jianghan Petroleum Institute,2004,26(4):110-111. [57] 李阳,王端平,李传亮.各向异性油藏的矢量井网[J].石油勘探与开发,2006,33(2):225-227. LI Yang,WANG Duanping,LI Chuanliang.Vectorial well arrangement in anisotropic reservoirs[J].Petroleum Exploration and Development,2006,33(2):225-227. [58] ZHANG Kai,ZHANG Hao,ZHANG Liming,et al.A new method for the construction and optimization of quadrangular adaptive well pattern[J].Computational Geosciences,2017,21(3):499-518. [59] 赵辉,唐乙玮,康志江,等.油藏开发生产优化近似扰动梯度升级算法[J].中国石油大学学报:自然科学版,2016,40(2):99-104. ZHAO Hui,TANG Yiwei,KANG Zhijiang,et al.Reservoir production optimization using an upgraded perturbation gradient approximation algorithm[J].Journal of China University of Petroleum:Edition of Natural Science,2016,40(2):99-104. [60] YETEN B,DURLOFSKY L J,AZIZ K.Optimization of nonconventional well type,location,and trajectory[J].SPE Journal,2003,8(3):200-210. [61] BOUZARKOUNA Z,DING D Y,AUGER A.Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models[J].Computational Geosciences,2012,16(1):75-92. [62] BELLOUT M C,VOLKOV O.Development of efficient constraint-handling approaches for well placement optimization[C]//Proceedings of the 16th European Conference on the Mathematics of Oil Recovery.Barcelona,Spain:European Association of Geoscientists & Engineers,2018:1-19. [63] FENG Qihong,LI Shanshan,ZHANG Xianmin,et al.Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm[J].Petroleum Science,2022,19(6):2879-2894. [64] 陈志刚,郎兆新.模糊综合评判法在油气田开发方案评价及优选中的应用[J].华东石油学院学报,1988,12(1):59-64. CHEN Zhigang,LANG Zhaoxin.An application of fuzzy comprehensive judgement to determination of oilfield development projects[J].Journal of East China Petroleum Institute,1988,12(1):59-64. [65] 刘德华.优选油气田开发方案的灰色评价模型[J].江汉石油学院学报,1989,11(4):37-42. LIU Dehua.Grey evaluation model for optimizing development projects of oil and gas field[J].Journal of Jianghan Petroleum Institute,1989,11(4):37-42. [66] 刘志斌,刘康生.注水开发油田的最优控制模型及求解[J].西南石油学院学报,1993,15(2):66-70. LIU Zhibin,LIU Kangsheng.Optimum control model of water injection developing oilfield and its solution[J].Journal of Southwest Petroleum Institute,1993,15(2):66-70. [67] 张在旭.油田开发最优规划模型的求解[J].石油大学学报:自然科学版,1998,22(4):112-113. ZHANG Zaixu.Solving method of optimum planning model for oilfield development[J].Journal of University of Petroleum,China,1998,22(4):112-113. [68] 胥泽银,郭科,龚灏.最优化方法在油田控水稳油中的应用[J].四川联合大学学报:工程科学版,1999,3(5):7-11. XU Zeyin,GUO Ke,GONG Hao.Application of optimal method to oil fieldcontrol water and consolidate oil[J].Journal of Sichuan University:Engineering Science Edition,1999,3(5):7-11. [69] LUO Jungang,QI Yutao,XIE Jiancang,et al.A hybrid multi-objective PSO-EDA algorithm for reservoir flood control operation[J].Applied Soft Computing,2015,34:526-538. [70] 张凯,李阳,姚军,等.油藏生产优化理论研究[J].石油学报,2010,31(1):78-83. ZHANG Kai,LI Yang,YAO Jun,et al.Theoretical research on production optimization of oil reservoirs[J].Acta Petrolei Sinica,2010,31(1):78-83. [71] JIA Deli,ZHANG Jiqun,WANG Quanbin,et al.Research on intelligent analysis approach of waterflooding for mature fields[C]//Proceedings of the 33rd Chinese Control and Decision Conference.Kunming:IEEE,2021. [72] WANG Zhongzheng,ZHANG Kai,CHEN Guodong,et al.Evolutionary-assisted reinforcement learning for reservoir real-time production optimization under uncertainty[J].Petroleum Science,2023,20(1):261-276. [73] 赵辉,刘邓,宋本彪,等.基于数据空间反演的油藏实时生产优化方法[J].石油学报,2022,43(1):67-74. ZHAO Hui,LIU Deng,SONG Benbiao,et al.Optimization method for real-time reservoir production based on data space inversion[J].Acta Petrolei Sinica,2022,43(1):67-74. [74] 杨剑锋,杜金虎,杨勇,等.油气行业数字化转型研究与实践[J].石油学报,2021,42(2):248-258. YANG Jianfeng,DU Jinhu,YANG Yong,et al.Research and practice on digital transformation of the oil and gas industry[J].Acta Petrolei Sinica,2021,42(2):248-258. [75] ZHAN Cheng,SANKARAN S,LEMOINE V,et al.Application of machine learning for production forecasting for unconventional resources[R].URTEC-2019-47,2019. [76] HOURFAR F,BIDGOLY H J,MOSHIRI B,et al.A reinforcement learning approach for waterflooding optimization in petroleum reservoirs[J].Engineering Applications of Artificial Intelligence,2019,77:98-116. [77] MIFTAKHOV R,AL-QASIM A,EFREMOV I.Deep reinforcement learning:reservoir optimization from pixels[R].IPTC 20151,2020. [78] ZHAO Xinggang,ZHANG Kai,CHEN Guodong,et al.Surrogate-assisted differential evolution for production optimization with nonlinear state constraints[J].Journal of Petroleum Science and Engineering,2020,194:107441. [79] 黄朝琴,年凯,王斌,等.一种考虑物理过程信息的油气渗流深度学习新模型[J].中国石油大学学报:自然科学版,2020,44(4):47-56. HUANG Zhaoqin,NIAN Kai,WANG Bin,et al.A novel deep learning model with physical process information for prediction of flow behaviors in oil and gas reservoirs[J].Journal of China University of Petroleum:Edition of Natural Science,2020,44(4):47-56. [80] FU T C.A review on time series data mining[J].Engineering Applications of Artificial Intelligence,2011,24(1):164-181. [81] ZHANG Kai,WANG Xiaoya,MA Xiaopeng,et al.The prediction of reservoir production based proxy model considering spatial data and vector data[J].Journal of Petroleum Science and Engineering,2022,208:109694. [82] RAISSI M,PERDIKARIS P,KARNIADAKIS G E.Physics-Informed neural networks:a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J].Journal of Computational Physics,2019,378:686-707. [83] ZHU Yinhao,ZABARAS N,KOUTSOURELAKIS P S,et al.Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data[J].Journal of Computational Physics,2019,394:56-81. [84] WATTER M,SPRINGENBERG J T,BOEDECKER J,et al.Embed to control:a locally linear latent dynamics model for control from raw images[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal:MIT Press,2015. [85] JIN Z L,LIU Yimin,DURLOFSKY L J.Deep-learning-based surrogate model for reservoir simulation with time-varying well controls[J].Journal of Petroleum Science and Engineering,2020,192:107273. [86] CHUA K,CALANDRA R,MCALLISTER R,et al.Deep reinforcement learning in a handful of trials using probabilistic dynamics models[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems.Montréal:Curran Associates Inc.,2018. |
| [1] | 刘喜武, 王心宇, 刘宇巍, 张金强, 刘炯, 刘倩. 中国陆相页岩油地震勘探技术现状及发展方向[J]. 石油学报, 2023, 44(12): 2270-2285. |
| [2] | 刘文岭, 韩大匡. 数字孪生油气藏:智慧油气田建设的新方向[J]. 石油学报, 2022, 43(10): 1450-1461. |
| [3] | 孙金声, 刘凡, 程荣超, 冯杰, 郝惠军, 王韧, 白英睿, 刘钦政. 机器学习在防漏堵漏中研究进展与展望[J]. 石油学报, 2022, 43(1): 91-100. |
| [4] | 李宁, 徐彬森, 武宏亮, 冯周, 李雨生, 王克文, 刘鹏. 人工智能在测井地层评价中的应用现状及前景[J]. 石油学报, 2021, 42(4): 508-522. |
| [5] | 王敏生, 光新军. 智能钻井技术现状与发展方向[J]. 石油学报, 2020, 41(4): 505-512. |
| [6] | 王家宏. 多油层油藏分层注水稳产条件与井网加密调整[J]. 石油学报, 2009, 30(1): 80-83. |
| [7] | 彭仕宓, 尹旭, 张继春, 李建敏, 任继波, 王凤刚. 注水开发中粘土矿物及其岩石敏感性的演化模式[J]. 石油学报, 2006, 27(4): 71-75. |
| [8] | 张继春, 彭仕宓, 穆立华, 孙以剑, 李建敏. 流动单元四维动态演化仿真模型研究[J]. 石油学报, 2005, 26(1): 69-73. |
| [9] | 王以法. 新型智能钻头的设计[J]. 石油学报, 2003, 24(1): 92-95. |
| [10] | 廖红伟, 王凤琴, 薛中天, 王琛. 基于大系统方法的油藏动态分析[J]. 石油学报, 2002, 23(6): 45-49. |
| [11] | 王以法. 人工智能钻井实时专家控制系统研究[J]. 石油学报, 2001, 22(2): 83-86. |
| [12] | 吴新根, 葛家理. 应用人工神经网络识别碳酸盐岩相[J]. 石油学报, 1996, 17(4): 50-54. |
| [13] | 张绍槐, 何华灿, 李琪, 郭建明. 石油钻井信息技术的智能化研究[J]. 石油学报, 1996, 17(4): 114-119. |
| [14] | 姜效典, 李巍然. 应用瓦尔什变换和人工神经网络划分岩层和岩性识别[J]. 石油学报, 1994, 15(4): 17-22. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||