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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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 25950 - 21
Hauptverfasser:	Wang, Zisong, Cheng, Zhiliang, Wang, Wenxiang, Ding, Xiujian, Xia, Lu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 17.07.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/4077 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
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Abstract	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.
AbstractList	Abstract 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. 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. 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.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.
ArticleNumber	25950
Author	Wang, Zisong Wang, Wenxiang Xia, Lu Ding, Xiujian Cheng, Zhiliang
Author_xml	– sequence: 1 givenname: Zisong surname: Wang fullname: Wang, Zisong email: wangzisong0033@gmail.com organization: School of Civil Engineering and Transportation, Weifang University – sequence: 2 givenname: Zhiliang surname: Cheng fullname: Cheng, Zhiliang organization: School of Civil Engineering and Transportation, Weifang University – sequence: 3 givenname: Wenxiang surname: Wang fullname: Wang, Wenxiang organization: School of Civil Engineering and Transportation, Weifang University – sequence: 4 givenname: Xiujian surname: Ding fullname: Ding, Xiujian organization: China University of Petroleum (East China) Geological College – sequence: 5 givenname: Lu surname: Xia fullname: Xia, Lu organization: School of Petroleum Engineering, Shandong University of Petrochemical Technology
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Keywords	Long short-term memory Parallel computing Chimp optimization algorithm Oil and gas extraction Well log analysis
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SubjectTerms	639/166 639/4077 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
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Title	Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China
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