郭军辉,郑宪宝,王治国,杨冰冰,付宪弟,马宏宇,朱吉军
GUO Junhui,ZHENG Xianbao,WANG Zhiguo,YANG Bingbing,FU Xiandi,MA Hongyu,ZHU Jijun

• 1:中国石油大庆油田有限责任公司勘探开发研究院

摘要(Abstract)：

数字化转型、智能化发展是老油田开发技术提档升级、提质增效的必要途径。全面总结了国内外油田开发技术智能化研究进展，介绍了人工智能技术在大庆长垣油田水驱开发中的应用进展及取得的重要成果，尤其强调了智能测井解释、智能井震结合储层预测、基于数据挖掘的注水优化调整以及措施井层智能优选等关键技术在提升油田开发效率、效果和经济效益方面的积极作用。通过技术创新，大庆长垣油田水驱自然递减率控制到7%以下，年含水上升值控制到0.2百分点以内，实现了特高含水后期油田的高水平、高质量开发。在此基础上，展望了水驱开发技术的智能化发展方向，指出应加快油气大语言模型的应用，加强实时动态监测技术的研发，并通过数字孪生模型、智能方案编制、注采智能优化等手段推动油田开发向更高水平的智能化转型。
Digital transition and intelligent development are necessary paths for upgrading levels and improving quality and efficiency of mature oilfields. Application progress and significant achievements of AI techniques in water flooding development in Daqing Placanticline oilfield are presented, with particular emphasis on positive effect of key techniques of intelligent well logging interpretation, reservoir prediction by intelligent well-seismic tie, water injection optimization adjustment based on data mining, and intelligent optimization of stimulation wells and reservoirs in enhancing efficiency, effectiveness and economic benefits of oilfield development. Through technological innovation, natural decline rate by water flooding in Daqing Placanticline oilfield is controlled to <7%, and annual water cut increase is controlled to less than 0.2 percentage points, realizing high-level and high-quality oilfield development in late stage of ultra-high water cut. On this basis, intelligent development direction of water flooding technology is prospected, suggesting that application of large language models should be accelerated for oil and gas, research and development of real-time dynamic monitoring techniques should be enhanced, and techniques of digital twin models, intelligent plan design and smart injection-production optimization should be adopted, so as to promote higher-level intelligent transform of oilfield development.

关键词(KeyWords)： 大庆长垣油田;水驱开发;智能化;关键技术;实践;展望
Daqing Placanticline oilfield;water flooding;intelligent;key technologies;practice;prospect

Abstract:

Keywords:

基金项目(Foundation): 中国石油天然气集团有限公司攻关性应用性科技专项“中高渗油田特高含水期大幅度提高采收率技术研究”（2023ZZ22）子课题“特高含水后期水驱持续高效开发技术研究与试验”（2023ZZ22YJ02）

作者(Author): 郭军辉,郑宪宝,王治国,杨冰冰,付宪弟,马宏宇,朱吉军
GUO Junhui,ZHENG Xianbao,WANG Zhiguo,YANG Bingbing,FU Xiandi,MA Hongyu,ZHU Jijun

DOI: 10.19597/J.ISSN.1000-3754.202403008

参考文献(References)：

• [1]檀朝东，刘合，高小永，等.中国陆上油气田生产智能化现状及展望[J].前瞻科技，2023,2(2):121-130.TAN Chaodong,LIU He,GAO Xiaoyong,et al. Current situation and prospects of intelligent production in onshore oil and gas fields in China[J]. Forward Technology, 2023, 2(2):121-130.
• [2]耿关庆.大庆油田智能化储运系统建设展望[J].油气田地面工程，2022,41(11):12-17.GENG Guanqing. Prospects of intelligent storage and transportation system construction in Daqing Oilfield[J]. Oil-Gas Field Surface Engineering,2022,41(11):12-17.
• [3]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.
• [4]鲍云波，张宽慧，杨跃跃.榆树林油田智能化建设思路探讨[J].油气田地面工程，2020,39(9):68-71.BAO Yunbo,ZHANG Kuanhui,YANG Yueyue. Discussion on intelligent construction idea of Yushulin oilfield[J]. Oil-Gas Field Surface Engineering,2020,39(9):68-71.
• [5]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,2020,29(1):1-10.
• [6]谢奇芳，张政.人工智能发展与展望[J].通信企业管理，2024,31(3):35-37.XIE Qifang,ZHANG Zheng. Development and Outlook of Artificial Intelligence[J]. Communication Enterprise Management,2024,31(3):35-37.
• [7]周军强.人工智能技术发展现状与展望[J].计算机应用文摘，2023,39(12):201-203.ZHOU Junqiang. Current status and prospects of artificial intelligence technology development[J]. Chinese Journal of Computer Application,2023,39(12):201-203.
• [8]李睿晶，房超，陈凯.新时代我国人工智能发展回顾与展望[J].科技智囊，2023,29(1):14-21.LI Ruijing,FANG Chao,CHEN Kai. Review and prospect of Chinese artificial intelligence development in the new era[J].Think Tank of Science&Technology,2023,29(1):14-21.
• [9] BERGEN K J,JOHNSON P A,DE HOOP M V,et al. Machine learning for data-driven discovery in solid Earth geoscience[J].Science,2019,363(6433):1299.
• [10] REICHSTEIN M,CAMPS-VALLS G,STEVENS B,et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature,2019,566(7743):195-204.
• [11]李宁，徐彬森，武宏亮，等.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.LI Ning,XU Binsen,WU Hongliang,et al. Application status and prospects of artificial intelligence in well logging and formation evaluation[J]. Acta Petrolei Sinica, 2021, 42(4):508-522.
• [12]程希，程宇雪，程佳豪，等.基于机器学习与大数据技术的地球物理测井系统[J].西安石油大学学报（自然科学版），2019,34(6):108-116.CHENG Xi,CHENG Yuxue,CHENG Jiahao,et al. Geophysical logging system based on machine learning and big data technology[J]. Journal of Xi’an Shiyou University(Natural Science Edition),2019,34(6):108-116.
• [13]程希，宋新爱，李国军，等.数据模型与物理模拟驱动的人工智能测井[J].测井技术，2021,45(3):233-239.CHENG Xi,SONG Xin’ai,LI Guojun,et al. Design and implementation of ecological clouds for artificial intelligence logging driven by big data and algorithms[J].Well Logging Technology,2021,45(3):233-239.
• [14] AKKURT R, MILLER M, HODENFIELD B, et al. Machine learning for well log normalization[C]. Calgary:SPE Annual Technical Conference and Exhibition, 2019.
• [15] ISHANK G,DEVEGOWDA D,JAYARAM V,et al. Machine learning regressors and their metrics to predict synthetic sonic and brittle zones[C]. Denver:SPE/AAPG/SEG Unconventional Resources Technology Conference,2019.
• [16]李健，杨明任，杜玉山，等.概率神经网络在盐水水淹层识别中的应用[J].油气地质与采收率，2022, 29(6):121-129.LI Jian,YANG Mingren,DU Yushan,et al.Application of probabilistic neural network in saline water flooded layer identification[J]. Petroleum Geology and Recovery Efficiency, 2022, 29(6):121-129.
• [17]赵邦六，雍学善，高建虎，等.中国石油智能地震处理解释技术进展与发展方向思考[J].中国石油勘探，2021,26(5):12-23.ZHAO Bangliu,YONG Xueshan,GAO Jianhu,et al. Progress and development direction of PetroChina intelligent seismic processing and interpretation technology[J]. China Petroleum Exploration,2021,26(5):12-23.
• [18] CUI R A,CAO D P,LIU Q,et al. VP and VS prediction from digital rock images using a combination of U-Net and convolutional neural networks[J]. Geophysics,2021,86(1):27-37.
• [19]常德宽，雍学善，王一惠，等.基于深度卷积神经网络的地震数据断层识别方法[J].石油地球物理勘探，2021,56(1):1-8.CHANG Dekuan,YONG Xueshan,WANG Yihui,et al. Seismic fault interpretation based on deep convolutional neural networks[J]. Oil Geophysical Prospecting,2021,56(1):1-8.
• [20]闫星宇，顾汉明，罗红梅，等.基于改进深度学习方法的地震相智能识别[J].石油地球物理勘探，2020,55(6):1169-1177.YAN Xingyu, GU Hanming, LUO Hongmei, et al. Intelligent seismic facies classification based on an improved deep learning method[J]. Oil Geophysical Prospecting, 2020, 55(6):1169-1177.
• [21]魏国华，韩宏伟，刘浩杰，等.基于半监督高斯混合模型与梯度提升树的砂岩储层相控孔隙度预测[J]，石油地球物理勘探，2023,58(1):46-55.WEI Guohua, HAN Hongwei, LIU Haojie, et al. Facies-controlled porosity prediction of sandstone reservoirs based on semisupervised Gaussian mixture model and gradient boosting tree[J]. Oil Geophysical Prospecting,2023,58(1):46-55.
• [22]刘合，李艳春，贾德利，等.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, et al. Application status and prospects of artificial intelligence in the refinement of waterflooding development program[J].Acta Petrolei Sinica, 2023, 44(9):1574-1586.
• [23]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.
• [24] ZHANG K,ZHANG H,ZHANG L M,et al. A new method for the construction and optimization of quadrangular adaptive well pattern[J]. Computational Geosciences, 2017, 21(3):499-518.
• [25]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.
• [26] DAI Q Y, ZHANG L M, ZHANG K, et al. An efficient infill well placement optimization approach for extra-low permeability reservoir[J]. Journal of Energy Resources Technology,2023,145(3):033001.
• [27] YETEN B,DURLOFSKY L J,AZIZ. Optimization of nonconventional well type, location, and trajectory[J]. SPE Journal,2003,8(3):200-210.
• [28] LIU W, LIU W D, GU J W. Reservoir inter-well connectivity analysis based on a data driven method[C]. Abu Dhabi:Abu Dhabi International Petroleum Exhibition&Conference,2019.
• [29] YOUSEF A A, GENTIL P, JERRY 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.
• [30] MOHAMMAD S,DANIAL K,JERRY L J. et al. Interwell connectivity evaluation in cases of changing skin and frequent production interruptions[J]. Journal of Petroleum Science and Engineering,2014,122:616-630.
• [31] GUO J H,WEI L Y,SUN L,et al. Estimation of interwell connectivity of a single layer in a stratified reservoir based on physical constraints[C]. Wuhan:International Field Exploration and Development Conference,2023.
• [32]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.
• [33] XUE X M, CHEN G D, ZHANG K, et al. A divide-and-conquer optimization paradigm for waterflooding production optimization[J]. Journal of Petroleum Science and Engineering,2022,211:110050.
• [34] JIA D L,ZHANG J Q,WANG Q B,et al. Research on intelligent analysis approach of waterflooding for mature fields[C].Kunming:33rd Chinese Control and Decision Conference,2021.
• [35]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.