Year: 2025
Type: article
Abstract: Coal seam porosity is a key parameter for evaluating the physical properties of coalbed methane (CBM) reservoirs, and its accurate prediction is crucial for CBM exploration and development. Traditional log interpretation methods exhibit limited accuracy when dealing with multifactor nonlinear relationships. This paper proposes a coal seam porosity prediction model based on an error back propagation (BP) neural network, which establishes the nonlinear relationship between optimized conventional logging curves (AC, CAL, CNL, DEN, GR, RT) and porosity (POR). Input variables were screened using Spearman correlation analysis, and a three-layer BP neural network structure was constructed. The network weights and thresholds were optimized via the back-propagation algorithm. Experimental results demonstrate that the model effectively learns the complex mapping relationship between logging parameters and porosity. The predicted results show strong agreement with measured values, with an average relative error of less than 8%, outperforming traditional regression methods. This approach provides a reliable technical means for predicting coal seam porosity.
DOI: 10.54097/jawsys62
Authors: Ping Zhou, Xinsi Hu, Danni Wei
Institution: Xi'an Shiyou University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414588385