Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China

Well-log analysis contributes significantly to effective oil and gas extraction, but inconsistent logs may render subsequent geological analyses useless. This study tackles this problem by devising a deep Long Short-Term Memory (LSTM) model that uses the new Parallel and Distributed Chimp Optimizati...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 25950 - 21
Main Authors: Wang, Zisong, Cheng, Zhiliang, Wang, Wenxiang, Ding, Xiujian, Xia, Lu
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 17.07.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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639/4077
Chimp optimization algorithm
Computers
Datasets
Distributed processing
Drilling
Efficiency
Geology
Humanities and Social Sciences
Long short-term memory
Machine learning
Missing data
multidisciplinary
Neural networks
Oil and gas extraction
Optimization algorithms
Parallel computing
Real time
Science
Science (multidisciplinary)
Well log analysis
Long short-term memory
Parallel computing
Chimp optimization algorithm
Oil and gas extraction
Well log analysis
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Well-log analysis contributes significantly to effective oil and gas extraction, but inconsistent logs may render subsequent geological analyses useless. This study tackles this problem by devising a deep Long Short-Term Memory (LSTM) model that uses the new Parallel and Distributed Chimp Optimization Algorithm (PDCOA). PDCOA’s primary goal is to speed up the process of hyperparameter tuning for LSTMs by letting them work in parallel and across multiple computers, with separate groups of computers communicating with each other regularly to ensure the system is diverse and reliable. It is designed for reconstructing missing well-log data, showing that the proposed method is more scalable, efficient, and accurate as a predictor. This feature makes it a valuable tool for geological interpretation and estimating hydrocarbon resources.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-11077-9