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Volume 13
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311

by
Xudong Hu
1,2,3,4,*,
Wangfeng Leng
1,
Kun Xiao
1,
Guo Song
1,
Yiming Wei
1 and
Changchun Zou
5,*
1
Nanchang Key Laboratory of Intelligent Sensing Technology and Instruments for Geological Hazards, East China University of Technology, Nanchang 330013, China
2
Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi’an Shiyou University, Xi’an 710065, China
3
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China
4
Key Laboratory of Gas Hydrate, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
5
School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(7), 1208; https://doi.org/10.3390/jmse13071208 (registering DOI)
Submission received: 3 June 2025 / Revised: 18 June 2025 / Accepted: 20 June 2025 / Published: 21 June 2025
(This article belongs to the Special Issue Advances in Marine Gas Hydrate Exploration and Discovery—2nd Edition)
Versions Notes

Abstract

Natural gas hydrates, with their efficient and clean energy characteristics, are deemed a significant pillar within the future energy sector, and their resource quantification and development have a profound impact on the transformation of global energy structure. However, how to accurately identify gas hydrate reservoirs (GHRs) is currently a hot research topic. This study explores the logging identification method of marine GHRs based on machine learning (ML) according to the logging data of the International Ocean Drilling Program (IODP) Expedition 311. This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. Among them, RF has the highest accuracy, precision, recall, and harmonic mean of precision and recall (F1 score), all of which are above 0.90. With an area under curve (AUC) of nearly 1 for RF, it is confirmed that ML technology is effective in this area. Research has shown that ML provides an alternative method for quickly and efficiently identifying GHRs based on well logging data and also offers a scientific foundation and technical backup for the future prospecting and mining of natural gas hydrates.
Keywords: reservoir identification; gas hydrates; well logging; machine learning; IODP Expedition 311 reservoir identification; gas hydrates; well logging; machine learning; IODP Expedition 311

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MDPI and ACS Style

Hu, X.; Leng, W.; Xiao, K.; Song, G.; Wei, Y.; Zou, C. Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311. J. Mar. Sci. Eng. 2025, 13, 1208. https://doi.org/10.3390/jmse13071208

AMA Style

Hu X, Leng W, Xiao K, Song G, Wei Y, Zou C. Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311. Journal of Marine Science and Engineering. 2025; 13(7):1208. https://doi.org/10.3390/jmse13071208

Chicago/Turabian Style

Hu, Xudong, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, and Changchun Zou. 2025. "Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311" Journal of Marine Science and Engineering 13, no. 7: 1208. https://doi.org/10.3390/jmse13071208

APA Style

Hu, X., Leng, W., Xiao, K., Song, G., Wei, Y., & Zou, C. (2025). Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311. Journal of Marine Science and Engineering, 13(7), 1208. https://doi.org/10.3390/jmse13071208

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