{"title":"Coal Seam Roof and Floor Lithology Prediction for Underground Coal Gasification Based on Deep Residual Shrinkage Network","authors":"Jialiang Guo, Ruizhao Yang, Fengtao Han","doi":"10.1002/ese3.2073","DOIUrl":null,"url":null,"abstract":"<p>Lithology identification is a crucial task in coal underground gasification projects, serving as a prerequisite for ensuring the safe operation of these endeavors. The inherent complexity in the relationship between logging parameters and lithological compositions creates ambiguity, leading to biases in traditional logging interpretation methodologies. We introduce a lithological prediction model, the deep residual shrinkage network (DRSN), which integrates residual networks, attention mechanisms, and soft-thresholding strategies. This network mitigates the gradient vanishing issue common in traditional neural networks and enhances the model's focus on essential features, thereby improving its ability to capture critical information. Acoustic, bulk density, neutron, gamma, and deep resistivity logs are used as inputs, with lithology as the output. A comparative analysis between the DRSN and other newer lithological prediction models is conducted. Blind well testing results demonstrate the superior performance of the DSRN, with higher Accuracy, Precision, Recall, and <i>F</i>1 Scores of 0.8221, 0.7198, 0.8004, and 0.7465, respectively. This study provides a novel and rapid method for lithology evaluation of strata in the early stages of underground coal gasification.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 3","pages":"1361-1374"},"PeriodicalIF":3.5000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.2073","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Science & Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ese3.2073","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Lithology identification is a crucial task in coal underground gasification projects, serving as a prerequisite for ensuring the safe operation of these endeavors. The inherent complexity in the relationship between logging parameters and lithological compositions creates ambiguity, leading to biases in traditional logging interpretation methodologies. We introduce a lithological prediction model, the deep residual shrinkage network (DRSN), which integrates residual networks, attention mechanisms, and soft-thresholding strategies. This network mitigates the gradient vanishing issue common in traditional neural networks and enhances the model's focus on essential features, thereby improving its ability to capture critical information. Acoustic, bulk density, neutron, gamma, and deep resistivity logs are used as inputs, with lithology as the output. A comparative analysis between the DRSN and other newer lithological prediction models is conducted. Blind well testing results demonstrate the superior performance of the DSRN, with higher Accuracy, Precision, Recall, and F1 Scores of 0.8221, 0.7198, 0.8004, and 0.7465, respectively. This study provides a novel and rapid method for lithology evaluation of strata in the early stages of underground coal gasification.
期刊介绍:
Energy Science & Engineering is a peer reviewed, open access journal dedicated to fundamental and applied research on energy and supply and use. Published as a co-operative venture of Wiley and SCI (Society of Chemical Industry), the journal offers authors a fast route to publication and the ability to share their research with the widest possible audience of scientists, professionals and other interested people across the globe. Securing an affordable and low carbon energy supply is a critical challenge of the 21st century and the solutions will require collaboration between scientists and engineers worldwide. This new journal aims to facilitate collaboration and spark innovation in energy research and development. Due to the importance of this topic to society and economic development the journal will give priority to quality research papers that are accessible to a broad readership and discuss sustainable, state-of-the art approaches to shaping the future of energy. This multidisciplinary journal will appeal to all researchers and professionals working in any area of energy in academia, industry or government, including scientists, engineers, consultants, policy-makers, government officials, economists and corporate organisations.