LAPSE:2023.36921
Published Article
LAPSE:2023.36921
A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning
Huayang Li, Qiang Tan, Jingen Deng, Baohong Dong, Bojia Li, Jinlong Guo, Shuiliang Zhang, Weizheng Bai
November 30, 2023
In recent years, there has been significant research and practical application of machine learning methods for predicting reservoir pore pressure. However, these studies frequently concentrate solely on reservoir blocks exhibiting normal-pressure conditions. Currently, there exists a scarcity of research addressing the prediction of pore pressure within reservoir blocks characterized by abnormally high pressures. In light of this, the present paper introduces a machine learning-based approach to predict pore pressure within reservoir blocks exhibiting abnormally high pressures. The methodology is demonstrated using the X block as a case study. Initially, the combination of the density−sonic velocity crossplot and the Bowers method is favored for elucidating the overpressure-to-compact mechanism within the X block. The elevated pressure within the lower reservoir is primarily attributed to the pressure generated during hydrocarbon formation. The Bowers method has been chosen to forecast the pore pressure in well X-1. Upon comparison with real pore pressure data, the prediction error is found to be under 5%, thus establishing it as a representative measure of the reservoir’s pore pressure. Intelligent prediction models for pore pressure were developed using the KNN, Extra Trees, Random Forest, and LightGBM algorithms. The models utilized five categories of well logging data, sonic time difference (DT), gamma ray (GR), density (ZDEN), neutron porosity (CNCF), and well diameter (CAL), as input. After training and comparison, the results demonstrate that the LightGBM model exhibits significantly superior performance compared to the other models. Specifically, it achieves R2 values of 0.935 and 0.647 on the training and test sets, respectively. The LightGBM model is employed to predict the pore pressure of two wells neighboring well X-1. Subsequently, the predicted data are juxtaposed with the actual pore pressure measurements to conduct error analysis. The achieved prediction accuracy exceeds 90%. This study delivers a comprehensive analysis of pore pressure prediction within sections exhibiting anomalously high pressure, consequently furnishing scientific insights to facilitate both secure and efficient drilling operations within the X block.
Keywords
empirical models, Extra Trees, KNN, LightGBM, Machine Learning, overpressure, pore pressure prediction, Random Forest, well logs
Subject
Suggested Citation
Li H, Tan Q, Deng J, Dong B, Li B, Guo J, Zhang S, Bai W. A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning. (2023). LAPSE:2023.36921
Author Affiliations
Li H: School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China; State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China [ORCID]
Tan Q: School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China; State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
Deng J: School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China; State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
Dong B: School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China; State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
Li B: School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102200, China; State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
Guo J: Shanghai Quartermaster and Energy Quality Supervision Station, Quartermaster and Energy Quality Supervision Station, Joint Logistics Support Force, Shanghai 200137, China
Zhang S: CNOOC Tianjin Branch, Tianjin 300459, China
Bai W: State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Journal Name
Processes
Volume
11
Issue
9
First Page
2603
Year
2023
Publication Date
2023-08-31
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr11092603, Publication Type: Journal Article
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LAPSE:2023.36921
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doi:10.3390/pr11092603
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Nov 30, 2023
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Nov 30, 2023
 
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