基于神经网络的莺歌海盆地DF区块黄流组储层压力预测与成因分析

宁卫科; 鞠玮; 相如

doi:10.11781/sysydz2024051088

基于神经网络的莺歌海盆地DF区块黄流组储层压力预测与成因分析

doi: 10.11781/sysydz2024051088

宁卫科^{1, 2,},
鞠玮^{1, 2, ,},
相如³

1.
煤层气资源与成藏过程教育部重点实验室, 江苏徐州 221008
2.
中国矿业大学资源与地球科学学院, 江苏徐州 221116
3.
中国科学院地理科学与资源研究所, 北京 100101

基金项目:

国家自然科学基金项目 42372185

国家自然科学基金项目 41971335

详细信息

作者简介:
宁卫科(1999—), 男, 硕士生, 从事裂缝预测、储层地质力学等学习和研究。E-mail: ts21010051a31@cumt.edu.cn

通讯作者:
鞠玮(1988—), 男, 博士, 教授, 博士生导师, 从事非常规油气储层地质力学教学与科研工作。E-mail: wju@cumt.edu.cn

中图分类号: TE122.23
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- 文章访问数: 108
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-31
- 修回日期: 2024-08-01
- 刊出日期: 2024-09-28

Pressure prediction and genesis analysis of Huangliu Formation reservoir in DF block of Yinggehai Basin based on neural networks

NING Weike^{1, 2
,},
JU Wei^{1, 2
, ,},
XIANG Ru³

1.
Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education, Xuzhou, Jiangsu 221008, China
2.
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
3.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

摘要

摘要: 在油气勘探开发及生产过程中，储层压力对油气聚集、分布及运移的过程起着重要作用，异常高压储层甚至会造成井壁崩落、井涌、井喷等钻井事故。传统的储层压力测井预测主要采用经验公式法、有效应力法等，存在参数确定难、主观性强等问题。为此，以莺歌海盆地DF区块为例，在实测数据基础上，构建基于BP神经网络和卷积神经网络的储层压力预测模型，建立测井曲线与实测储层压力之间的隐式直接关系，对储层压力进行了预测并分析了其超压成因。研究结果表明：(1)构建的卷积神经网络模型预测储层压力精度高，最优模型的均方根误差为0.27 MPa；(2)预测莺歌海盆地DF区块黄流组储层压力为53.26~55.60 MPa，平均压力系数为1.66~1.95，呈现为超压；(3) DF区块黄流组超压成因机制为以流体膨胀作用为主，欠压实作用为辅。
- 储层压力预测 /
- BP神经网络 /
- 卷积神经网络 /
- 超压 /
- 黄流组 /
- 莺歌海盆地
Abstract: In the process of oil and gas exploration, development and production, reservoir pressure plays a crucial role in the accumulation, distribution and migration of oil and gas. Abnormally high-pressure reservoirs can lead to drilling accidents such as wellbore collapse, kicks and blowouts. Traditional methods for predicting reservoir pressure, mainly based on well logging calculations using empirical formula and effective stress methods, suffer from drawbacks including complex parameter identification and significant subjectivity. Consequently, the paper uses the DF block in the Yinggehai Basin as a case study, building a reservoir pressure prediction model based on real-time pressure data using both the BP neural network and convolutional neural network. This process established an implicit direct relationship between logging curves and real-time reservoir pressure, allowing for the prediction of reservoir pressure and an analysis of the causes of overpressure. The results of the study indicate that: (1) The established convolutional neural network model demonstrates high accuracy in predicting reservoir pressure, with a root mean square error of 0.27 MPa for the optimal model. (2) The predicted reservoir pressure range for the Huangliu Formation in the DF block of the Yinggehai Basin is 53.26-55.60 MPa, with an average pressure coefficient of 1.66-1.95, consistent with overpressure. (3) The mechanism behind the overpressure in the Huangliu Formation, DF block, is mainly due to fluid expansion, supplemented by undercompaction.
- reservoir pressure prediction /
- BP neural network /
- convolutional neural network /
- overpressure /
- Huangliu Formation /
- Yinggehai Basin
注释:

利益冲突声明/Conflict of Interests

作者鞠玮是本刊青年编委会成员，未参与本文的同行评审或决策。

Author JU Wei is a Young Editorial Board Member of this journal. JU Wei did not take part in peer review or decision making of this article.

作者贡献/Authors’Contributions

宁卫科负责论文写作与BP神经网络方法研究；鞠玮负责论文思路和修改；相如负责卷积神经网络方法研究及实现。所有作者均阅读并同意最终稿件的提交。

NING Weike is responsible for manuscript writing and BP neural network method research. JU Wei is responsible for the idea and revision of the manuscript. XIANG Ru is responsible for the study and implementation of convolutional neural network method. All authors have read the last version of the paper and consented to its submission.

HTML全文

图 1 莺歌海盆地DF区块构造单元分布及地层柱状图

a.构造单元分布；b.地层柱状图；c.DF区块井位分布。

Figure 1. Structural unit distribution and composite columnar section of DF block in the Yinggehai Basin

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图 2 卷积神经网络结构

Figure 2. Architecture of convolutional neural network (CNN)

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图 3 三层BP神经网络结构

Figure 3. Architecture of three-layer back-propagation (BP) neural network

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图 4 输入参数与储层压力的相关性

Figure 4. Correlation between input parameters and formation pressure

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图 5 模型预测效果

Figure 5. Model prediction effect

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图 6 莺歌海盆地DF区块2口典型井黄流组储层压力预测结果

Figure 6. Predicted reservoir pressure for two typical wells in DF block, Yinggehai Basin

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图 7 莺歌海盆地DF区块地层沉积速率

Figure 7. Sedimentation rate of strata in DF block, Yingehai Basin

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图 8 莺歌海盆地DF区块黄流组地层泥质含量分布

Figure 8. Distribution of clay content in Huangliu Formation of DF block, Yinggehai Basin

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图 9 莺歌海盆地DF区块黄流组地层温度分布

Figure 9. Distribution of formation temperature in Huangliu Formation of DF block, Yinggehai Basin

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图 10 莺歌海盆地DF区块部分钻井声波时差和密度测井曲线响应特征

Figure 10. Response characteristics of acoustic time difference and density logging curve in selected wells in DF block, Yinggehai Basin

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图 11 声波速度—密度交会图异常高压机制^[42]

Figure 11. Anomalous high-pressure mechanism of acoustic velocity and density crossplots

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图 12 莺歌海盆地DF区块典型井声波速度－密度交会图

Figure 12. Acoustic and velocity density crossplots in typical wells of DF block, Yinggehai Basin

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表 1 BP神经网络和卷积神经网络模型预测效果对比

Table 1. Contrast in prediction performance between BP neural network and CNN models

测井参数组合	RMSE值/MPa
测井参数组合	BP神经网络	卷积神经网络
GR、RHOB、P40H、TNPH、DT	0.63	0.27
GR、P40H、TNPH、DT	0.61	0.29
GR、RHOB、TNPH、DT	0.73	0.35
GR、TNPH、DT	2.07	1.06

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表 2 部分卷积神经网络预测压力与实测压力对比

Table 2. Comparison of predicted and real-time pressure using convolutional neural networks

深度/m	自然伽马/API	深电阻率/(Ω·m)	密度/(g/cm³)	中子	声波时差/(μs/ft)	实测压力/MPa	预测压力/MPa
2 905.00	73.40	3.59	2.37	0.19	92.48	56.27	55.82
3 054.38	113.49	3.09	2.55	0.17	84.90	52.91	53.00
3 059.98	74.24	7.92	2.32	0.18	89.74	52.92	53.71
3 072.83	102.52	4.65	2.45	0.17	84.70	52.98	52.81
3 074.02	103.11	5.26	2.47	0.15	82.85	52.98	53.00
3 094.60	75.70	15.13	2.29	0.15	89.42	53.01	53.16
3 116.99	78.94	7.29	2.32	0.17	85.89	53.06	53.02
3 117.99	78.02	7.21	2.33	0.16	88.44	53.05	53.24
3 121.09	85.07	11.26	2.29	0.15	89.41	53.07	52.95
3 180.52	103.64	3.71	2.53	0.17	80.28	52.74	52.89
3 187.92	128.29	3.17	2.55	0.17	80.72	52.77	53.02
3 191.51	72.33	6.93	2.31	0.16	83.89	53.22	53.11
3 206.48	81.29	4.29	2.35	0.18	85.97	53.37	53.15
3 213.62	74.66	3.29	2.39	0.21	81.79	53.43	53.26
3 262.92	69.77	2.71	2.36	0.20	81.96	53.44	53.49

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表 3 莺歌海盆地DF区块黄流组储层压力预测结果

Table 3. Predicted reservoir pressure in Huangliu Formation of DF block, Yingehai Basin

井号	顶深/m	底深/m	平均储层压力/MPa	平均储层压力系数
1-14	2 778.10	2 999.69	53.81	1.86
1-2	2 839.06	3 140.35	54.64	1.83
1-3	2 795.02	2 921.81	53.82	1.88
1-5	3 090.06	3 228.29	54.24	1.72
1-6	2 756.76	2 946.04	54.12	1.90
1-10	2 750.10	2 935.20	54.00	1.90
2-2	2 995.12	3 159.56	54.23	1.76
2-4	3 105.10	3 478.10	54.72	1.66
2-6	2 997.56	3 238.35	53.69	1.72
2-8	2 973.10	3 202.00	53.69	1.74
2-1	2 600.00	3 103.30	55.61	1.96
1-4	2 783.35	2 927.73	53.26	1.87

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参考文献(46)

[1]	宋瑞有, 裴健翔, 王立锋, 等. 莺歌海盆地东方区海底扇勘探开发可视化剖析[J]. 天然气地球科学, 2023, 34(12): 2172-2183. SONG Ruiyou, PEI Jianxiang, WANG Lifeng, et al. Visualization analysis of exploration and development of the submarine fan in Dongfang area of Yinggehai Basin[J]. Natural Gas Geoscience, 2023, 34(12): 2172-2183.
[2]	柳广弟. 石油地质学[M]. 5版. 北京: 石油工业出版社, 2018. LIU Guangdi. Petroleum geology[M]. 5th ed. Beijing: Petroleum Industry Press, 2018.
[3]	BOWERS G L. Detecting high overpressure[J]. The Leading Edge, 2002, 21(2): 174-177. doi: 10.1190/1.1452608
[4]	张光亚, 马锋, 梁英波, 等. 全球深层油气勘探领域及理论技术进展[J]. 石油学报, 2015, 36(9): 1156-1166. ZHANG Guangya, MA Feng, LIANG Yingbo, et al. Domain and theory-technology progress of global deep oil & gas exploration[J]. Acta Petrolei Sinica, 2015, 36(9): 1156-1166.
[5]	贾新峰, 杨贤友, 周福建, 等. 孔隙压力预测方法在油气田开发中的应用[J]. 天然气技术, 2009, 3(2): 31-33. JIA Xinfeng, YANG Xianyou, ZHOU Fujian, et al. The application of pore pressure prediction to field development[J]. Natural Gas Technology, 2009, 3(2): 31-33.
[6]	HOTTMANN C E, JOHNSON R K. Estimation of formation pressures from log-derived shale properties[J]. Journal of Petroleum Technology, 1965, 17(6): 717-722. doi: 10.2118/1110-PA
[7]	EATON B A. The effect of overburden stress on geopressure prediction from well logs[J]. Journal of Petroleum Technology, 1972, 24(8): 929-934. doi: 10.2118/3719-PA
[8]	EATON B A. Graphical method predicts geopressures worldwide[J]. World Oil, 1976, 183(1): 100-104.
[9]	ZHANG J C, STANDIFIRD W, LENAMOND C. Casing ultradeep, ultralong salt sections in deep water: a case study for failure diagnosis and risk mitigation in record-depth well[C]//Proceedings of the SPE Annual Technical Conference and Exhibition. Denver: SPE, 2008.
[10]	GUTIERREZ M A, BRAUNSDOR N R, COUZENS B A. Calibration and ranking of pore-pressure prediction models[J]. The Leading Edge, 2006, 25(12): 1516-1523. doi: 10.1190/1.2405337
[11]	ZHANG J C. Effective stress, porosity, velocity and abnormal pore pressure prediction accounting for compaction disequilibrium and unloading[J]. Marine and Petroleum Geology, 2013, 45: 2-11. doi: 10.1016/j.marpetgeo.2013.04.007
[12]	BOWERS G L. Pore pressure estimation from velocity data: accoun-ting for overpressure mechanisms besides undercompaction[J]. SPE Drilling & Completion, 1995, 10(2): 89-95.
[13]	于浩. 多变量孔隙压力预测与不确定性分析方法及应用研究[D]. 武汉: 中国地质大学, 2020. YU Hao. Multivariate pore-pressure prediction and uncertainty analysis[D]. Wuhan: China University of Geosciences, 2020.
[14]	金浩, 马劲风, 李琳, 等. 渤东低凸起南段地层压力预测方法研究[J]. 地球物理学进展, 2024, 39(2): 788-799. JIN Hao, MA Jinfeng, LI Lin, et al. Study on the prediction method of formation pressure in the southern part of the Bodong low uplift[J]. Progress in Geophysics, 2024, 39(2): 788-799.
[15]	宋先知, 姚学喆, 李根生, 等. 基于LSTM-BP神经网络的地层孔隙压力计算方法[J]. 石油科学通报, 2022, 7(1): 12-23. doi: 10.3969/j.issn.2096-1693.2022.01.002 SONG Xianzhi, YAO Xuezhe, LI Gensheng, et al. A novel method to calculate formation pressure based on the LSTM-BP neural network[J]. Petroleum Science Bulletin, 2022, 7(1): 12-23. doi: 10.3969/j.issn.2096-1693.2022.01.002
[16]	罗发强, 刘景涛, 陈修平, 等. 基于BP和LSTM神经网络的顺北油田5号断裂带地层孔隙压力智能预测方法[J]. 石油钻采工艺, 2022, 44(4): 506-514. LUO Faqiang, LIU Jingtao, CHEN Xiuping, et al. Intelligent method for predicting formation pore pressure in No. 5 fault zone in Shunbei oilfield based on BP and LSTM neural network[J]. Oil Drilling & Production Technology, 2022, 44(4): 506-514.
[17]	林英松, 王臣, 徐路. 基于BP神经网络的裂缝性地层压力预测方法[J]. 西部探矿工程, 2012, 24(10): 101-102. doi: 10.3969/j.issn.1004-5716.2012.10.033 LIN Yingsong, WANG Chen, XU Lu. A method for predicting fractured formation pressure based on BP neural network[J]. West-China Exploration Engineering, 2012, 24(10): 101-102. doi: 10.3969/j.issn.1004-5716.2012.10.033
[18]	HUTOMO P S, ROSID M S, HAIDAR M W. Pore pressure prediction using Eaton and neural network method in carbonate field "X" based on seismic data[J]. IOP Conference Series: Materials Science and Engineering, 2019, 546(3): 032017. doi: 10.1088/1757-899X/546/3/032017
[19]	HADI F, ECKERT A, ALMAHDAWI F. Real-time pore pressure prediction in depleted reservoirs using regression analysis and artificial neural networks[C]//Proceedings of the SPE Middle East Oil and Gas Show and Conference. Manama: SPE, 2019.
[20]	RADWAN A E, WOOD D A, RADWAN A A. Machine learning and data-driven prediction of pore pressure from geophysical logs: a case study for the Mangahewa gas field, New Zealand[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2022, 14(6): 1799-1809. doi: 10.1016/j.jrmge.2022.01.012
[21]	YU H, CHEN G X, GU H M. A machine learning methodology for multivariate pore-pressure prediction[J]. Computers & Geosciences, 2020, 143: 104548.
[22]	RASHIDI M, ASADI A. An artificial intelligence approach in estimation of formation pore pressure by critical drilling data[C]//Proceedings of the 52nd U.S. Rock Mechanics/Geomechanics Symposium. Seattle: ARMA, 2018.
[23]	ABDULMALEK A S, ELKATATNY S, ABDULRAHEEM A, et al. Pore pressure prediction while drilling using fuzzy logic[C]//Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. Dammam: SPE, 2018.
[24]	李雨. 基于机器学习的油田水驱地层压力预测方法研究[D]. 大庆: 东北石油大学, 2023. LI Yu. Study on prediction method of formation pressure in oilfield water drive based on machine learning[D]. Daqing: Northeast Petroleum University, 2023.
[25]	徐建永, 赵牛斌, 徐仕琨, 等. 莺歌海盆地中新统海相烃源岩发育主控因素及模式[J]. 地质科技通报, 2021, 40(2): 54-63. XU Jianyong, ZHAO Niubin, XU Shikun, et al. Main controlling factors and development model of the Miocene marine source rocks in Yinggehai Basin[J]. Bulletin of Geological Science and Technology, 2021, 40(2): 54-63.
[26]	张旭友, 范彩伟, 郭小文, 等. 莺歌海盆地中央底辟带乐东区莺歌海组超压成因及相对贡献定量化评价[J/OL]. 地球科学, 2022. http://kns.cnki.net/kcms/detail/42.1874.P.20220217.1915.031.html. ZHANG Xuyou, FAN Caiwei, GUO Xiaowen, et al. Overpressure mechanisms and quantitative evaluation of the relative contribution for Yinggehai Formation in Ledong area of the central diapir zone, Yinggehai Basin[J/OL]. Earth Science, 2022. http://kns.cnki.net/kcms/detail/42.1874.P.20220217.1915.031.html.
[27]	HUANG B J, XIAO X M, LI X X. Geochemistry and origins of natural gases in the Yinggehai and Qiongdongnan basins, offshore South China Sea[J]. Organic Geochemistry, 2003, 34(7): 1009-1025. doi: 10.1016/S0146-6380(03)00036-6
[28]	李绪深, 杨计海, 范彩伟, 等. 南海北部海域高温超压天然气勘探新进展与关键技术: 以莺歌海盆地乐东斜坡带为例[J]. 中国海上油气, 2020, 32(1): 23-31. LI Xushen, YANG Jihai, FAN Caiwei, et al. New progress and key technologies for high temperature and overpressure natural gas exploration in the northern part of South China Sea: taking the Ledong Slope Belt of Yinggehai Basin as an example[J]. China Offshore Oil and Gas, 2020, 32(1): 23-31.
[29]	黄保家, 黄合庭, 李里, 等. 莺—琼盆地海相烃源岩特征及高温高压环境有机质热演化[J]. 海相油气地质, 2010, 15(3): 11-18. HUANG Baojia, HUANG Heting, LI Li, et al. Characteristics of marine source rocks and effect of high temperature and overpressure to organic matter maturation in Yinggehai-Qiongdongnan Basins[J]. Marine Origin Petroleum Geology, 2010, 15(3): 11-18.
[30]	TONG Chuanxin, XIE Yuhong, HUANG Zhilong, et al. Geochemical behaviors of HPHT gas reservoirs in the Yinggehai Basin and the efficient gas accumulation mode in its diapir flanks[J]. Natural Gas Industry B, 2015, 2(2/3): 144-154.
[31]	毛倩茹, 范彩伟, 罗静兰, 等. 超压背景下中深层砂岩储集层沉积—成岩演化差异性分析: 以南海莺歌海盆地中新统黄流组为例[J]. 古地理学报, 2022, 24(2): 344-360. MAO Qianru, FAN Caiwei, LUO Jinglan, et al. Analysis of sedimentary-diagenetic evolution difference on middle-deep buried sandstone reservoirs under overpressure background: a case study of the Miocene Huangliu Formation in Yinggehai Basin, South China Sea[J]. Journal of Palaeogeography (Chinese Edition), 2022, 24(2): 344-360.
[32]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[33]	吴正阳, 莫修文, 柳建华, 等. 裂缝性储层分级评价中的卷积神经网络算法研究与应用[J]. 石油物探, 2018, 57(4): 618-626. WU Zhengyang, MO Xiuwen, LIU Jianhua, et al. Convolutional neural network algorithm for classification evaluation of fractured reservoirs[J]. Geophysical Prospecting for Petroleum, 2018, 57(4): 618-626.
[34]	何鹏程. 改进的卷积神经网络模型及其应用研究[D]. 大连: 大连理工大学, 2015. HE Pengcheng. Research of improved convolutional neural network model and its application[D]. Dalian: Dalian University of Technology, 2015.
[35]	RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
[36]	王玮卿. 基于BP神经网络的页岩气水平井地应力计算研究[D]. 大庆: 东北石油大学, 2021. WANG Weiqing. Research on calculation of in-situ stress of shale gas horizontal well based on BP neural network[D]. Daqing: Northeast Petroleum University, 2021.
[37]	HECHT-NIELSEN R. Applications of counterpropagation networks[J]. Neural Networks, 1988, 1(2): 131-139.
[38]	黄洪林, 李军, 张更, 等. 莺歌海盆地斜坡带全井段孔隙压力预测方法[J]. 石油钻采工艺, 2022, 44(4): 401-407. HUANG Honglin, LI Jun, ZHANG Geng, et al. Method for predicting pore pressure of whole well interval in slope zone in Yinggehai Basin[J]. Oil Drilling & Production Technology, 2022, 44(4): 401-407.
[39]	BARKER C. GEOLOGICAL NOTES: Aquathermal pressuring-role of temperature in development of abnormal-pressure zones[J]. AAPG Bulletin, 1972, 56(10): 2068-2071.
[40]	何盼情. 马海东及周缘地区Pt-E₃gt下地层压力特征及超压成因[D]. 西安: 西安石油大学, 2021. HE Panqing. Formation pressure characteristics and overpressure genesis of Pt-E₃¹ formation in Mahaidong and its surrounding areas[D]. Xi'an: Xi'an Shiyou University, 2021.
[41]	郭书生, 陈现军, 廖高龙, 等. 莺歌海盆地地层超压成因与定量评价方法[J]. 中国石油大学学报(自然科学版), 2022, 46(6): 143-148. GUO Shusheng, CHEN Xianjun, LIAO Gaolong, et al. A quantitative evaluation method for predicting polygenetic overpressure in Yinggehai Basin[J]. Journal of China University of Petroleum (Edition of Natural Science), 2022, 46(6): 143-148.
[42]	BOWERS G L. Determining an appropriate pore-pressure estimation strategy[C]//Proceedings of the Offshore Technology Conference. Houston: OTC, 2001.
[43]	LI Chao, LUO Xiaorong, ZHANG Likuan, et al. Overpressure generation mechanisms and its distribution in the paleocene Shahejie Formation in the Linnan Sag, Huimin Depression, Eastern China[J]. Energies, 2019, 12(16): 3183.
[44]	侯志强, 张书平, 李军, 等. 西湖凹陷中部西斜坡地区超压成因机制[J]. 石油学报, 2019, 40(9): 1059-1068. HOU Zhiqiang, ZHANG Shuping, LI Jun, et al. Genetic mechanism of overpressures in the west slope of central Xihu Sag[J]. Acta Petrolei Sinica, 2019, 40(9): 1059-1068.
[45]	宫亚军, 张奎华, 曾治平, 等. 准噶尔盆地阜康凹陷侏罗系超压成因、垂向传导及油气成藏[J]. 地球科学, 2021, 46(10): 3588-3600. GONG Yajun, ZHANG Kuihua, ZENG Zhiping, et al. Origin of overpressure, vertical transfer and hydrocarbon accumulation of jurassic in Fukang Sag, Junggar Basin[J]. Earth Science, 2021, 46(10): 3588-3600.
[46]	HUA Yanqi, GUO Xiaowen, TAO Ze, et al. Mechanisms for overpressure generation in the Bonan Sag of Zhanhua Depression, Bohai Bay Basin, China[J]. Marine and Petroleum Geology, 2021, 128: 105032.