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: 81
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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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