摘要
煤体结构的测井精准识别有助于深部煤层气勘探开发和层位优选.为了准确、恰当地运用地球物理测井识别煤体结构,本文重点分析了国内相关成果,对煤体结构分类、测井响应及其判识方法进行了述评.结果发现:原生结构煤的井径(CAL)为14.82-47.29 cm,密度(DEN)为1.18～2.08 g/cm3,自然伽马(GR)为 8.66～111.45 API,声波时差(AC)为 259.00～681.21 μs/m,中子(CNL)为 22.95 ％～52.76％,侧向电阻率(LLD)为39.18～26 990.50 Ω·m.随着煤体结构破碎程度的加剧,DEN、CNL和LLD值减小,GR、CAL和AC值增大.聚类分析、多元线性回归、贝叶斯判别式、BP神经网络、机器学习和支持向量机等定量方法能够显著提高测井识别煤体结构的准确度.最后,建议煤体结构精准判识应加强地球物理测井的曲线重构理论、资料标准化、系统化和数据化研究,构建可视化、智能化、跨区域煤体结构测井判识的数据提取、指标优选和结果存储体系.
Abstract
The precise identification of coal body structure through logging is helpful for the exploration and development of deep coalbed methane and the optimization of horizons.In order to accurately and appropriately use geophysical logging to identify coal structure,this paper reviews relevant domestic achievements,the classification,logging response and identification methods of coal structure.The results showed that the wellbore diameter(CAL)of primary structured coal varies from 14.82 cm to 47.29 cm,density(DEN)ranges from 1.18 g/cm3 to 2.08 g/cm3,natural gamma(GR)is between 8.66 API and 11.45 API,acoustic time difference(AC)ranges from 259.00 μs/m to 681.21 μs/m,neutron(CNL)is between 22.95％and 52.76％,and lateral resistivity(LLD)varies from 39.18 Ω·m to 26 990.50 Ω·m.As the degree of coal structure fragmentation intensifies,the DEN,CNL,and LLD values decrease,while the GR,CAL and AC values increase.Quantitative methods such as cluster analysis,multiple linear regression,Bayesian discriminant analysis,BP neural network,machine learning,and support vector machine can significantly improve the accuracy of logging recognition of coal structure.Finally,it is suggested that the precise identification of coal structure should strengthen the research on curve reconstruction theo-ry,data standardization,systematization and dataization of geophysical logging,and build a visualized,intelligent,cross regional coal structure logging identification data extraction,in-dex optimization and result storage system.
基金项目
国家重点研发计划(2021YFC2902005)
煤层气资源与成藏过程教育部重点实验室(中国矿业大学)开放基金(2022-006)
中国煤炭地质总局科技基金(ZMKJ-2019-J13)
NETL
NSTL
维普
万方数据