School of Resources and Environment, Henan Polytechnic University
Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization
The Collaborative Innovation Center of Coalbed Methane(Shale Gas) of Central Plains Economic Region
School of Energy Science and Engineering, Henan Polytech⁃nic University

目的目的煤层气产能主要受地质和工程因素影响,阐明这些因素对煤层气井产能的影响机制是实现储层精细改造和煤层气井提产的基础。方法方法本文以沁水盆地柿庄南区块为研究对象,综合考虑地质背景、储层物性和动态排采数据,利用神经网络算法开展煤层气产能预测。首先,利用灰色关联分析法遴选出10个地质参数作为煤层气产能预测的主控因素,在此基础上,运用模糊数学法实现研究区34口煤层气井富集区划分,最后,根据分类结果,结合实际排采数据,分别利用BP(backpropagation)和LSTM(longshort-termmemory)神经网络算法实现煤层气井日产气量预测。结果结果结果表明:(1)渗透率、含气饱和度和储层压力梯度等10个参数是影响研究区煤层气产气性能的关键因素;(2)利用模糊数学评价方法评价煤层气的富集,可将研究区34口井产气效果划分为有利区、较有利区和不利区;(3)依托LSTM算法建立了煤储层日产气量预测模型,预测误差值为4.06%~14.79%,平均误差值为11.09%,预测精度明显高于BP神经网络模型,结论结论根据LSTM算法建立的煤储层日产气量预测模型稳定性好且预测精度高,可作为煤储层产能长程预测的一种有效手段,进而为煤层气开发工艺布施与排采方案制定提供科学依据。

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.

国家自然科学基金资助项目(41972175);河南省高校科技创新团队项目(21IRTSTHN007);河南省高校基本科研业务费专项基金项目(NSFRF220204)

金毅,郑晨晖,宋慧波,等.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.

JIN Y, ZHENG C H, SONG H B, et al. Prediction of coalbed methane productivity based on neural network models[J]. Journal of Henan Polytechnic University (Natural Science),2025,44(1):46-56.