李静;刘晨;刘惠民;许艳超;解丽;黄贵朋;

• 1:中国石油大学(华东)储运与建筑工程学院
• 2:中国石化胜利油田分公司油气勘探管理中心

摘要(Abstract)：

准噶尔盆地西部车排子地区石炭系火成岩储层断层多,且走向多变,复杂程度高,亟需开展地应力分布规律及其影响因素研究.本文以苏13井三维区石炭系火成岩储层为例,基于盒维数法,用分维值D对研究区构造复杂程度进行了表征;基于BP神经网络方法,通过MATLAB实现了边界约束的优化反演;利用COMSOL Multiphyics软件模拟分析了渗流-应力耦合作用下地应力分布规律及其影响因素,认为研究区地应力分布受断层数量、断层走向和石油开采的影响较大;考虑渗流-应力耦合作用后,地应力值增大,而地应力方向基本保持不变或发生微小偏转.研究结果表明:研究区发育三级、四级、五级及五级以下逆断层,断层相互切割,断层分布的分维值为1.811 1,分维值较大,研究区整体构造复杂.随着断层数量的增加,地应力值呈减小趋势;走向与荷载作用方向平行的断层内部最大水平主应力值较其余走向断层小,对附近应力场影响作用也较小;走向与荷载作用方向垂直的断层对附近应力场影响范围最大;考虑石油开采的影响后,最大水平主应力值增大;随着石油开采的深入,井口附近的地应力持续增大,但单井开采对地应力的影响范围有限.未考虑渗流-应力耦合作用,研究区最大水平主应力为21.4～30.8 MPa,最小水平主应力为14.5～20.1 MPa;考虑渗流-应力耦合作用后,最大水平主应力为22.7～32.3 MPa,最小水平主应力为15.3～21.8 MPa.研究区最大水平主应力方向在东北区域为北东东-南西西向,经过F1断层后地应力方向发生偏转,变为北东-南西向;在走向与荷载作用方向平行的断层内部和附近,地应力方向发生较大偏转.

关键词(KeyWords)： 复杂断层构造区;地应力;分维值;神经网络;数值模拟;渗流-应力耦合

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41972138);; 国家科技重大专项(ZD2019-183-007,2016ZX05002-002)

作者(Author): 李静;刘晨;刘惠民;许艳超;解丽;黄贵朋;

Email:

DOI: 10.13247/j.cnki.jcumt.001244

参考文献(References)：

• [1] 李志鹏,刘显太,杨勇,等.渤南油田低渗透储集层岩性对地应力场的影响[J].石油勘探与开发,2019,46(4):693-702.LI Zhipeng,LIU Xiantai,YANG Yong,et al.Influences of lithology on in-situ stress field in low permeability reservoirs in Bonanoilfield,Bohai Bay Basin,China[J].Petroleum Exploration and Development,2019,46(4):693-702.
• [2] 刘钰洋,刘诗琦,潘懋,等.基于三维角点网格模型的现今地应力有限元模拟[J].北京大学学报(自然科学版),2019,55(4):643-653.LIU Yuyang,LIU Shiqi,PAN Mao,et al.Research ofcrustal stress simulation using finite element analysis based on corner point grid[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2019,55(4):643-653.
• [3] JU W,LI Z L,SUN W F,et al.In-situ stress orientations in the Xiagou tight oil reservoir of Qingxi oilfield,Jiuxi Basin,northwestern China[J].Marine and Petroleum Geology,2018,98:258-269.
• [4] 李静,周汉国,刘思萌,等.基于数字图像处理的非均质岩石材料细观尺度应力分析[J].中国石油大学学报(自然科学版),2016,40(6):143-149.LI Jing,ZHOU Hanguo,LIU Simeng,et al.Stress analysis of heterogeneous rock material at mesoscopic scale based on digital image processing technology[J].Journal of China University of Petroleum(Edition of Natural Science),2016,40(6):143-149.
• [5] KONG D H,WU F Q,SAROGLOU C.Automatic identification and characterization of discontinuities in rock masses from 3D point clouds[J/OL].Engineering Geology,2020,265:105442[2020-09-21].https://doi.org/10.1016/j.enggeo.2019.105442.
• [6] SESETTY V,GHASSEMI A.Effect of rock anisotropy on wellbore stresses and hydraulic fracture propagation[J].International Journal of Rock Mechanics and Mining Sciences,2018,112:369-384.
• [7] 宫清顺,刘占国,庞旭,等.砂砾岩油藏储层非均质性及对剩余油分布影响:以柴达木盆地昆北油田切12区为例[J].中国矿业大学学报,2019,48(1):165-174.GONG Qingshun,LIU Zhanguo,PANG Xu,et al.Heterogeneity of sandy conglomerate reservoir and its influence on remaining oil distribution:A case study of Qie 12 block of Kunbei oilfield in Qaidam basin[J].Journal of China University of Mining & Technology,2019,48(1):165-174.
• [8] STEPHANSSON O.Rock stress in the Fennoscandian shield[J].Rock Testing and Site Characterization,1993(2):445-459.
• [9] SUGAWARA K,OBARA Y.Measuring rock stress:Case examples of rock engineering in Japan[J].Comprehensive Rock Engineering,1993,3:533-552.
• [10] AL-SHAMALI A,AL-MAYYAS E A,MURTHY N,et al.Geomechanical characterization of a matured deep jurassic carbonate reservoir:Explaining stress effects on production induced fault slip and reservoir development[C]// SPE Reservoir Characterisation and Simulation Conference and Exhibition.Abu Dhabi:Society of Petroleum Engineers,2015:461-477.
• [11] 李静,查明,刘震.基于声波测井资料的地应力分布研究:以饶阳凹陷任北奥陶系潜山为例[J].岩土力学,2011,32(9):2765-2770.LI Jing,ZHA Ming,LIU Zhen.Research on crustal stress distribution based on acoustic logging data--Taking north region of Renqiu Ordovician Buried Hill of Raoyang depression for example[J].Rock and Soil Mechanics,2011,32(9):2765-2770.
• [12] 付晓飞,孙兵,王海学,等.断层分段生长定量表征及在油气成藏研究中的应用[J].中国矿业大学学报,2015,44(2):271-281.FU Xiaofei,SUN Bing,WANG Haixue,et al.Fault segmentation growth quantitative characterization and its application on sag hydrocarbon accumulation research[J].Journal of China University of Mining & Technology,2015,44(2):271-281.
• [13] 刘中春,吕心瑞,李玉坤,等.断层对地应力场方向的影响机理[J].石油与天然气地质,2016,37(3):387-393.LIU Zhongchun,LYU Xinrui,LI Yukun,et al.Mechanism of faults acting on in situ stress field direction[J].Oil & Gas Geology,2016,37(3):387-393.
• [14] 赵毅鑫,卢志国,朱广沛,等.考虑主应力偏转的采动诱发断层活化机理研究[J].中国矿业大学学报,2018,47(1):73-80.ZHAO Yixin,LU Zhiguo,ZHU Guangpei,et al.Fault reactive induced by the principal stress rotation for the underground coal mining[J].Journal of China University of Mining & Technology,2018,47(1):73-80.
• [15] 张广明,熊春明,刘合,等.复杂断块地应力场数值模拟方法研究[J].断块油气田,2011,18(6):710-713.ZHANG Guangming,XIONG Chunming,LIU He,et al.Numerical simulation method for in-situ stress field in complex fault block[J].Fault-block Oil & Gas Field,2011,18(6):710-713.
• [16] 沈海超,程远方,王京印,等.断层扰动下地应力场三维有限元约束优化反演[J].岩土力学,2007,28(增1):359-365.SHEN Haichao,CHENG Yuanfang,WANG Jingyin,et al.3-D FEM inversion for the in-situ stress field around complex faults based on the theory of constrained optimization[J].Rock and Soil Mechanics,2007,28(Sup 1):359-365.
• [17] 徐珂,戴俊生,商琳,等.南堡凹陷现今地应力特征及影响因素[J].中国矿业大学学报,2019,48(3):570-583.XU Ke,DAI Junsheng,SHANG Lin,et al.Characteristics and influencing factors of in-site stress of Nanpu sag,Bohai Bay basin,China[J].Journal of China University of Mining & Technology,2019,48(3):570-583.
• [18] 徐珂,戴俊生,冯建伟,等.南堡凹陷高深北区三维非均质应力场精细预测[J].中国矿业大学学报,2018,47(6):1276-1286.XU Ke,DAI Junsheng,FENG Jianwei,et al.Prediction of 3D heterogeneous in-situ stress field of northern area in Gaoshen,Nanpu sag,Bohai Bay basin,China[J].Journal of China University of Mining & Technology,2018,47(6):1276-1286.
• [19] 张广明,刘合,张劲,等.储层流固耦合的数学模型和非线性有限元方程[J].岩土力学,2010,31(5):1657-1662.ZHANG Guangming,LIU He,ZHANG Jing,et al.Mathematical model and nonlinear finite element equation for reservoir fluid-solid coupling[J].Rock and Soil Mechanics,2010,31(5):1657-1662.
• [20] SONG F Q,HU X,JI K,et al.Effect of fluid-solid coupling on shale mechanics and seepage laws[J].Natural Gas Industry B,2018,5(1):41-47.
• [21] 翁剑桥,曾联波,吕文雅,等.断层附近地应力扰动带宽度及其影响因素[J].地质力学学报,2020,26(1):39-47.WENG Jianqiao,ZENG Lianbo,LYU Wenya,et al.Width of stress disturbed zone near fault and its influencing factors[J].Journal of Geomechanics,2020,26(1):39-47.
• [22] 周长所,耿亚楠,雷杨,等.渤中19-6复杂断块构造地应力分布规律试验研究[J].石油化工应用,2020,39(1):84-89.ZHOU Changsuo,GENG Yanan,LEI Yang,et al.Experimental research on distribution of in-situ stress of Bozhong 19-6 complex fault block[J].Petrochemical Industry Application,2020,39(1):84-89.
• [23] 邓攀,陈孟晋,杨泳.分形方法对裂缝性储集层的定量预测研究和评价[J].大庆石油地质与开发,2006,25(2):18-20.DENG Pan,CHEN Mengjin,YANG Yong.Theapplication of fractal approach to the quantitative estimation research and evaluation of fractured reservoir[J].Petroleum Geology & Oil Field Development in Daqing,2006,25(2):18-20.
• [24] ZHOU X P,ZHAO Z.Digital evaluation of nanoscale-pore shale fractal dimension with microstructural insights into shale permeability[J].Journal of Natural Gas Science and Engineering,2020,75:103137.
• [25] XIA Y X,CAI J C,PERFECT E,et al.Fractal dimension,lacunarity and succolarity analyses on CT images of reservoir rocks for permeability prediction[J/OL].Journal of Hydrology,2019,579:124198[2020-09-21].https://doi.org/10.1016/j.jhydrol.2019.124198.
• [26] 闫相祯,王保辉,杨秀娟,等.确定地应力场边界载荷的有限元优化方法研究[J].岩土工程学报,2010,32(10):1485-1490.YAN Xiangzhen,WANG Baohui,YANG Xiujuan,et al.Research on finite element optimization method for determining boundary loads of In-situ stress field[J].Chinese Journal of Geotechnical Engineering,2010,32(10):1485-1490.
• [27] 闻新.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003:18-29.WEN Xin.MATLABneural network simulation and application[M].Beijing:Science Press,2003:18-29.
• [28] JOHNSON L M,REZAEE R,KADKHODAIE A,et al.Geochemical property modelling of a potential shale reservoir in the Canning Basin (Western Australia),usingartificial neural networks and geostatistical tools[J].Computers & Geosciences,2018,120:73-81.
• [29] NIU H T.Smart safety early warning model of landslide geological hazard based on BP neural network[J].Safety Science,2020,123:104572.
• [30] BALLABIO D,VASIGHI M.A MATLAB toolbox forself organizing maps and supervised neural network learning strategies[J].Chemometrics & Intelligent Laboratory Systems,2012,118:24-32.
• [31] MITTAL M,BORA B,SAXENA S,et al.Performance prediction of PV module using electrical equivalent model and artificial neural network[J].Solar Energy,2018,176:104-117.