Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms

Guojie Zhang

doi:10.47297/taposatWSP2633-456913.20260703

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Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms

7卷第3期

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Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms
Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms
Weifang Commercial College Shandong Province, Weifan, Shandong, 261000, China
2026年7卷第3期页码：61-66
纸质出版：2026-03-30
DOI：10.47297/taposatWSP2633-456913.20260703
Accepted：

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Guojie Zhang. (2026). Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms. 科技理论与实践（英文版）, 7(3), 61-66.

Zhang Guojie.Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms[J].Theory and Practice of Science and Technology,2026,07(03):61-66.
Guojie Zhang. (2026). Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms. 科技理论与实践（英文版）, 7(3), 61-66. DOI： 10.47297/taposatWSP2633-456913.20260703.

Zhang Guojie.Research on Intelligent Prediction of Formation Pressure and Log Curve Completion Based on Machine Learning Algorithms[J].Theory and Practice of Science and Technology,2026,07(03):61-66. DOI： 10.47297/taposatWSP2633-456913.20260703.

摘要

Abstract

Aiming at the problem of low accuracy in formation pressure prediction caused by missing logging data and severe noise interference in oil and gas fields

this paper integrates deep learning with geological prior knowledge and proposes an integrated method for log curve completion and intelligent formation pressure prediction. The method implements multi-dimensional data preprocessing through wavelet threshold denoising

adaptive hierarchical normalization and SMOTE oversampling

constructs a Cascaded LSTM model to accurately complete missing segments of logging curves

designs a PC-LSTM model for intelligent formation pressure prediction

and introduces an EnLSTM model to solve the problems of small-sample training and prediction uncertainty quantification. Verified by field data from the Daqing Oilfield

Williston Basin (USA) and Eagle Ford Shale Reservoir: the signal-to-noise ratio of data reaches 21.36 dB after wavelet denoising; the coefficient of determination (R²) of Cascaded LSTM completion is 0.931; the mean absolute error of PC-LSTM pressure prediction is only 0.82 MPa; EnLSTM achieves an accuracy of 88.5% and a 95% confidence interval coverage rate of 92.3% with only 10% training samples. This method realizes the collaborative optimization of logging data restoration and formation pressure prediction

and can provide technical support for the safe and efficient exploration and development of complex oil and gas reservoirs.

关键词

Keywords

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