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

NING Weike; JU Wei; XIANG Ru

doi:10.11781/sysydz2024051088

Volume 46 Issue 5

Sep. 2024

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NING Weike, JU Wei, XIANG Ru. Pressure prediction and genesis analysis of Huangliu Formation reservoir in DF block of Yinggehai Basin based on neural networks[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(5): 1088-1097. doi: 10.11781/sysydz2024051088

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NING Weike, JU Wei, XIANG Ru. Pressure prediction and genesis analysis of Huangliu Formation reservoir in DF block of Yinggehai Basin based on neural networks[J]. PETROLEUM GEOLOGY & EXPERIMENT, 2024, 46(5): 1088-1097. doi: 10.11781/sysydz2024051088

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

doi: 10.11781/sysydz2024051088

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

Received Date: 2023-07-31
Rev Recd Date: 2024-08-01
Publish Date: 2024-09-28

Abstract

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

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.
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.

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References(46)

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

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

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