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Coal Geology & Exploration

Abstract

The accurate evaluation of reservoir fracability is an essential prerequisite for the fracturing design and post-fracturing productivity evaluation of reservoirs. Rock mechanical parameters have been applied to the fracability evaluation of shales presently, exhibiting great field application performance. Accordingly, it is crucial to obtain accurate rock mechanical parameters. This study developed a physics-informed neural network (PINN) model. Driven by data and physical information, the PINN model can accurately predict rock mechanical parameters using only a small amount of data. To verify its performance, the PINN model was compared with the artificial neural network, random forest, and XGBoost models. The comparison results show that the PINN model yielded an average accuracy greater than 95%, outperforming other models. Using the PINN model, this study obtained four rock mechanical parameters, namely modulus of elasticity, Poisson's ratio, tensile strength, and fracture toughness. Given the influence of rock mechanical parameters on reservoir fracability, this study developed an evaluation method for reservoir fracability based on the brittleness index and mechanical parameters. This fracability evaluation method was applied to reservoirs in the K2 member in the Cangdong sag of the Bohai Bay Basin. The evaluation results indicate generally high fracability of the study area. Specifically, lamellar mixed shales showed a fracability index of higher than 0.7, indicating high fracability, while lamellar felsic shales and thickly and thinly laminated shales comprising calcareous and dolomitic rocks of equal amounts exhibited fracability indices of 0.4‒0.7, indicating moderate fracability. The comparison between the evaluation results and the daily oil production of various reservoirs at the construction site verified the reliability of the smart fracability evaluation method developed in this study. Therefore, this fracability evaluation method can be applied to the fracability evaluation of shale reservoirs.

Keywords

shale oil researvoirs, rock mechanical parameter, fracability, machine learning, physics-informed

DOI

10.12363/issn.1001-1986.23.02.0106

Reference

[1] 李玉伟,龙敏,汤继周,等. 考虑裂尖塑性区影响的水力压裂缝高计算模型[J]. 石油勘探与开发,2020,47(1):175−185.

LI Yuwei,LONG Min,TANG Jizhou,et al. A hydraulic fracture height mathematical model considering the influence of plastic region at fracture tip[J]. Petroleum Exploration and Development,2020,47(1):175−185.

[2] MAO Shaowen,ZHANG Zhuo,CHUN T,et al. Field–scale numerical investigation of proppant transport among multicluster hydraulic fractures[J]. SPE Journal,2021,26(1):307−323.

[3] MAO Shaowen,SIDDHAMSHETTY P,ZHANG Zhuo,et al. Impact of proppant pumping schedule on well production for slickwater fracturing[J]. SPE Journal,2020,26(1):342−358.

[4] 刘合,黄有泉,蔡萌,等. 松辽盆地古龙页岩油储集层压裂改造工艺实践与发展建议[J]. 石油勘探与开发,2023,50(3):603−612.

LIU He,HUANG Youquan,CAI Meng,et al. Practice and development suggestions of hydraulic fracturing technology in the Gulong shale oil reservoirs of Songliao Basin,NE China[J]. Petroleum Exploration and Development,2023,50(3):603−612.

[5] MENG Siwei,LI Dongxu,LIU Xin,et al. Study on dynamic fracture growth mechanism of continental shale under compression failure[J]. Gas Science and Engineering,2023,114:204983.

[6] 李玉伟,彭根博,陈勉,等. CO2泡沫压裂井筒气–液–固三相流动模型[J]. 石油学报,2022,43(3):386−398.

LI Yuwei,PENG Genbo,CHEN Mian,et al. Gas–liquid–solid three phase flow model of CO2 foam fracturing in wellbore[J]. Acta Petrolei Sinica,2022,43(3):386−398.

[7] LIU Xin,MENG Siwei,LIANG Zhengzhao,et al. Microscale crack propagation in shale samples using focused ion beam scanning electron microscopy and three–dimensional numerical modeling[J]. Petroleum Science,2022,13(4):23.

[8] 张哲豪,李新,赵建斌,等. 页岩油储层岩石物理实验技术现状及发展[J]. 测井技术,2022,46(6):656−663.

ZHANG Zhehao,LI Xin,ZHAO Jianbin,et al. Current situation and development of petrophysical experiment technology in shale oil reservoir[J]. Well Logging Technology,2022,46(6):656−663.

[9] NIKRACESH P E. Computer–aided analysis of mechanical systems[M]. Prentice–Hall,Inc. ,1988,2(2):11.

[10] ALAJMI B N,AHMED K H,ADAM G P,et al. Modular multilevel inverter with maximum power point tracking for grid connected photovoltaic application[C]//IEEE International Symposium on Industrial Electronics,2011:2057–2062.

[11] RUSE C M,AHMADOV J,LIU Ning,et al. An integrated analytics and machine learning solution for predicting the anisotropic static geomechanical properties of the Tuscaloosa Marine Shale[C]//2021 SPE/AAPG/SEG Unconventional Resources Technology Conference,2021.

[12] ZHOU Jian,LI Xibing,MITRI H S. Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction[J]. Natural Hazards,2015,79(1):291−316.

[13] CAO Jing,GAO Juncheng,RAD H N,et al. A novel systematic and evolved approach based on XGBoost–firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock[J]. Engineering with Computers,2021,5(38):3829−3845.

[14] DEHGHAN S,SATTARI G,CHEHREH C S,et al. Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks[J]. Mining Science and Technology,2010,20(1):41−46.

[15] TARIQ Z,ELKATATNY S,MAHMOUD M,et al. A new artificial intelligence based empirical correlation to predict sonic travel time[C]//International Petroleum Technology Conference,2016.

[16] 曾晓华,孟迪,彭文丰,等. 北部湾盆地涠西南凹陷W油田储层综合评价研究[J]. 石油科学通报,2023,8(1):20−31.

ZENG Xiaohua,MENG Di,PENG Wenfeng,et al. Comprehensive evaluation of reservoirs in W oilfield in the southwest depression of Beibu Gulf Basin[J]. Petroleum Science Bulletin,2023,8(1):20−31.

[17] XIE Chiyu,DU Shuyi,WANG Jiulong,et al. Intelligent modeling with physics–informed machine learning for petroleum engineering problems[J]. Advances in Geo–Energy Research,2023,8(2):71−75.

[18] 徐梓辰,金衍,刘晓敏. 水基页岩抑制剂烷基糖苷季铵盐的页岩强度维持机理研究[J]. 石油科学通报,2020,5(1):67−77.

XU Zichen,JIN Yan,LIU Xiaomin. Study of inhibition performance and the mechanism of action of alkyl glucoside quaternary ammonium salt as a new shale inhibitor[J]. Petroleum Science Bulletin,2020,5(1):67−77.

[19] 周立宏,赵贤正,柴公权,等. 陆相页岩油效益勘探开发关键技术与工程实践:以渤海湾盆地沧东凹陷古近系孔二段为例[J]. 石油勘探与开发,2020,47(5):1059−1066.

ZHOU Lihong,ZHAO Xianzheng,CHAI Gongquan,et al. Key exploration & development technologies and engineering practice of continental shale oil:A case study of Member 2 of Paleogene Kongdian Formation in Cangdong Sag,Bohai Bay Basin,East China[J]. Petroleum Exploration and Development,2020,47(5):1059−1066.

[20] 陈立雄,董兴蒙. 基于改进人工蜂群的高精度纵波慢度提取方法[J]. 测井技术,2022,46(6):664−668.

CHEN Lixiong,DONG Xingmeng. High–precision compression wave slowness extraction based on improved artificial bee colony[J]. Well Logging Technology,2022,46(6):664−668.

[21] 王斌,邓继新,刘喜武,等. 矿物组分对龙马溪组页岩动、静态弹性特征的影响[J]. 地球物理学报,2019,62(12):4833−4845.

WANG Bin,DENG Jixin,LIU Xiwu,et al. The influence of rock composition on dynamic and static elastic properties of Longmaxi Formation shales[J]. Chinese Journal of Geophysics,2019,62(12):4833−4845.

[22] 赵斌,王芝银,伍锦鹏. 矿物成分和细观结构与岩石材料力学性质的关系[J]. 煤田地质与勘探,2013,41(3):59−63.

ZHAO Bin,WANG Zhiyin,WU Jinpeng. Relation between mineralogical composition and microstructure to the mechanical properties of rock materials[J]. Coal Geology & Exploration,2013,41(3):59−63.

[23] 石林,史璨,田中兰,等. 中石油页岩气开发中的几个岩石力学问题[J]. 石油科学通报,2019,4(3):223−232.

SHI Lin,SHI Can,TIAN Zhonglan,et al. Several rock mechanics problems in the development of shale gas in PetroChina[J]. Petroleum Science Bulletin,2019,4(3):223−232.

[24] ZHANG Yihuai,LEBEDEV M,AL–KYASERI A,et al. Characterization of nanoscale rock mechanical properties and microstructures of a Chinese sub–bituminous coal[J]. Journal of Natural Gas Science & Engineering,2018,52:106−116.

[25] 李曦宁,李剑平,沈金松,等. 基于电成像测井的碳酸盐岩储层孔隙结构识别新方法[J]. 测井技术,2022,46(6):689−695.

LI Xining,LI Jianping,SHEN Jinsong,et al. A novel identification method of pore structure in carbonate reservoirs based on electric imaging logging[J]. Well Logging Technology,2022,46(6):689−695.

[26] BREIMAN L. Random forests[J]. Machine Learning,2001,45(1):5−32.

[27] CHEN Tianqi,HE Tong. Xgboost:Extreme gradient boosting[J]. R package version 0. 4–2,2015,1(4):1−4.

[28] HE Houtian,GAO Shangce,JIN Ting,et al. A seasonal–trend decomposition–based dendritic neuron model for financial time series prediction[J]. Applied Soft Computing,2021,108:107488.

[29] 金衍,陈勉,张旭东. 利用测井资料预测深部地层岩石断裂韧性[J]. 岩石力学与工程学报,2001,20(4):454−456.

JIN Yan,CHEN Mian,ZHANG Xudong. Determination of fracture toughness for deep well rock with geophysical logging data[J]. Chinese Journal of Rock Mechanics and Engineering,2001,20(4):454−456.

[30] 满轲,周宏伟. 不同赋存深度岩石的动态断裂韧性与拉伸强度研究[J]. 岩石力学与工程学报,2010,29(8):1657−1663.

MAN Ke,ZHOU Hongwei. Research on dynamic fracture toughness and tensile strength of rock at different depths[J]. Chinese Journal of Rock Mechanics and Engineering,2010,29(8):1657−1663.

[31] SLOTA–VALIM M. Static and dynamic elastic properties,the cause of the difference and conversion methods–case study[J]. Nafta–Gaz,2015,71(11):816−826.

[32] RICKMAN R,MULLEN M,PETRE E,et al. A practical use of shale petrophysics for stimulation design optimization:All shale plays are not clones of the Barnett Shale[C]//SPE Annual Technical Conference and Exhibition,2008.

[33] 袁俊亮,邓金根,张定宇,等. 页岩气储层可压裂性评价技术[J]. 石油学报,2013,34(3):523−527.

YUAN Junliang,DENG Jingen,ZHANG Dingyu,et al. Fracability evaluation of shale–gas reservoirs[J]. Acta Petrolei Sinica,2013,34(3):523−527.

[34] 韩文中,赵贤正,金凤鸣,等. 渤海湾盆地沧东凹陷孔二段湖相页岩油甜点评价与勘探实践[J]. 石油勘探与开发,2021,48(4):777−786.

HAN Wenzhong,ZHAO Xianzheng,JIN Fengming,et al. Sweet spots evaluation and exploration of lacustrine shale oil of the second member of Paleogene Kongdian Formation in Cangdong Sag,Bohai Bay Basin[J]. Petroleum Exploration and Development,2021,48(4):777−786.

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