河南理工大学学报（自然科学版）2025，Vol.44Issue(1)：46-56,11.DOI:10.16186/j.cnki.1673-9787.2023030083
基于神经网络模型的煤层气产能预测研究
Prediction of coalbed methane productivity based on neural network models
摘要
Abstract
Objectives The productivity of coalbed methane is mainly affected by geological and engineering factors.Clarifying the influence mechanism of these factors on the productivity of coalbed methane wells is the basis for achieving fine reservoir reconstruction and increasing production of coalbed methane wells.Methods Therefore,this paper takes Shizhuang South Block in Qinshui Basin as the research object,and comprehensively considers the geological background,reservoir physical properties and dynamic drainage data,uses neural network algorithm to carry out CBM productivity prediction.Firstly,10 geological param-eters were selected as the main controlling factors for CBM productivity prediction by grey correlation analy-sis.On this basis,the fuzzy mathematics method was used to realize the division of 34 coalbed methane wells in the study area.Finally,according to the classification results,combined with the actual drainage data,the BP and LSTM neural network algorithms were used to predict the daily gas production of CBM wells.Results The results show that:(1)Based on the grey correlation method model analysis,10 param-eters such as permeability,gas saturation and reservoir pressure gradient in the study area are the key fac-tors affecting the gas production performance of coalbed methane;(2)Using fuzzy mathematics evaluation method to evaluate the enrichment of coalbed methane,the gas production effects of 34 wells in the study area is divided into three categories:favorable area,relatively favorable area and unfavorable area.(3)A coal reservoir daily gas production prediction model was established based on the LSTM algorithm,with a prediction error value between 4.06%and 14.79%,and the average error value of 11.09%.The prediction accuracy is significantly higher than the BP model.Conclusions The model has good stability and high pre-diction accuracy.It can be used as an effective means for long-term prediction of coal reservoir producti-vity,and then provide scientific basis for deployment of coalbed methane development processes and the formulation of procurement plans.the formulation of coalbed methane development plan and the scientific deployment of drainage technology.关键词
LSTM神经网络/BP神经网络/灰色关联分析/产能预测Key words
LSTM neural network/BP neural network/grey correlation analysis/productivity prediction分类
地质学引用本文复制引用
金毅,郑晨晖,宋慧波,马家恒,杨运航,刘顺喜,张昆,倪小明..基于神经网络模型的煤层气产能预测研究[J].河南理工大学学报（自然科学版）,2025,44(1):46-56,11.基金项目
国家自然科学基金资助项目(41972175) （41972175）
河南省高校科技创新团队项目(21IRTSTHN007) （21IRTSTHN007）
河南省高校基本科研业务费专项基金项目(NSFRF220204) （NSFRF220204）
