Research on Coal Structure Prediction Method Based on Genetic Algorithm–BP Neural Network

• Published in: Applied sciences Vol. 15; no. 5; p. 2514
• Main Authors: Wang, Cunwu, Peng, Xiaobo, Han, Gang, Zhao, Yan, Zhu, Yihao, Zhao, Ming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accuracy; Algorithms; Analysis; Classification; Coal; coal core data; coal structure prediction; Coalbed methane; Computational linguistics; Deep learning; Fault lines; Genetic algorithms; Genetic research; Geology; Identification; Language processing; Machine learning; Methods; Natural language interfaces; Neural networks; Permeability; Support vector machines; well logging curve; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15052514

This paper proposes a coal structure prediction technology based on deep learning, which uses logging data to achieve single-well prediction of the coal structure. This paper introduces the genetic algorithm (GA) to optimize the BP neural network, which can speed up its convergence to the global optimal solution, improve its training speed, and avoid the problems of easily producing the local optimal value and requiring a long training time. Taking the main coal seam of the Shizhuang block in the south of the Qinshui Basin as the research object and using the coal core data and logging data of nine parameter wells, the mapping relationship between the logging curve and coal structure is constructed based on the GA-BP neural network structure, and the coal structure is predicted. The prediction results are highly consistent with the coal structure measured from coal core sampling, with only a small error, and the prediction accuracy is 90%. It is shown that the GA-BP neural network structure can be used to effectively identify the coal structure, as well as predict the coal structure of uncored wells. Moreover, the findings of this study will be helpful for efforts to study the distribution law of the coal structure.