河北地质大学学报2026，Vol.49Issue(1)：52-58,7.DOI:10.13937/j.cnki.hbdzdxxb.2026.01.006
基于SSA-CNN模型的煤储层含气量预测方法研究
Research on Gas Content Prediction Method for Coal Reservoirs Based on an SSA-CNN Model:A Case Study From the Benxi Formation in Block M,Eastern Ordos Basin
林曦 1陈月春 2魏丹妮1
作者信息
- 1. 西安石油大学地球科学与工程学院,陕西 西安 710065
- 2. 西安石油大学陕西省油气成藏地质学重点实验室,陕西 西安 710065
- 折叠
摘要
Abstract
Accurate prediction of gas content in deep coal reservoirs is of significant engineering value for the efficient development of coalbed methane.However,reliance on a single geophysical parameter often overlooks the control of internal structures and heterogeneity of coal reservoirs on gas content,leading to deviations between predicted results and measured values,which fail to meet the precision requirements for efficient deep coalbed methane extraction.Based on in-depth mining of geophysical information,this study introduces an intrinsic mechanistic parameter of coal reservoirs-ash content-as a supplement to geological characteristics,thereby compensating for the inadequacy of geophysical information and constructing a multimodal feature system for gas content prediction.Taking the No.8 coal reservoir of the Carboniferous Benxi Formation in Block M on the eastern margin of the Ordos Basin as the study object,a convolutional neural network optimized by the Sparrow Search Algorithm(SSA-CNN)was employed to automatically extract spatial features of the data and establish a high-precision gas content prediction model.The results indicate that:1)Through Spearman nonlinear correlation analysis,six factors with significant influence on gas content were selected and determined as inputs for the prediction model;2)The SSA-CNN model achieved a prediction accuracy of R2=0.817 on the test set,with the mean absolute error reduced by 1.067％compared to the traditional CNN model.Practical research and analysis demonstrate that the SSA-CNN model can be effectively applied to high-precision gas content prediction in coal reservoirs and has promotional value for gas content prediction in regions with similar geological backgrounds.关键词
灰分/含气量/本溪组/麻雀搜索算法/卷积神经网络/鄂尔多斯盆地Key words
ash content/gas content/Benxi Formation/sparrow search algorithm/convolutional neural network/Ordos Basin分类
天文与地球科学引用本文复制引用
林曦,陈月春,魏丹妮..基于SSA-CNN模型的煤储层含气量预测方法研究[J].河北地质大学学报,2026,49(1):52-58,7.
