基于ResNet残差神经网络识别的深部煤层显微组分和微裂缝分类——以鄂尔多斯盆地石炭系本溪组8~#煤层为例  

Classification of macerals and microfractures in deep coal seams based on ResNet:A case study of the No.8 coal seam of the Carboniferous Benxi Formation in the Ordos Basin

作　　者：刘大锰[1,2] 王子豪 陈佳明 邱峰 朱凯 高羚杰 周柯宇 许少博 孙逢瑞 LIU Dameng;WANG Zihao;CHEN Jiaming;QIU Feng;ZHU Kai;GAO Lingjie;ZHOU Keyu;XU Shaobo;SUN Fengrui(School of Energy Resources,China University of Geosciences(Beijing),Beijing 100083,China;Beijing Key Laboratory of Unconventional Natural Gas Geological Evaluation and Development Engineering,Beijing 100083,China)

机构地区：[1]中国地质大学(北京)能源学院,北京100083 [2]非常规天然气地质评价与开发北京市重点实验室,北京100083

出　　处：《石油与天然气地质》2024年第6期1524-1536,共13页Oil & Gas Geology

基　　金：国家自然科学基金项目(42130806)。

摘　　要：显微组分和微裂缝是煤储层重要的微观特征,影响煤储层产气能力和力学性质。采集鄂尔多斯盆地深部煤层气井石炭系本溪组8^(#)煤层样品,运用ResNet残差神经网络识别方法,研究了显微组分和微裂缝发育特征。在煤样305个显微组分和65个微裂缝图样本研究的基础上,建立了基于残差神经网络识别的煤岩显微组分和微裂缝识别方法,并利用残差神经网络技术对镜下数据进行反演,构建了深部煤储层显微组分和微裂缝的识别和分类模型。结合地质特征和聚类算法结果联合验证,模型具有可靠性。显微组分预测准确率为0.90,微裂缝预测准确率为0.80,可以有效预测煤岩显微组分和微裂缝类型。模型识别与预测表明裂缝形态与显微组分具有相关关系。裂缝的发育与显微组分中的镜质组关系最大,裂缝类别和数量的预测结果与显微组分发育的相吻合。Macerals and microfractures are identified as important microscopic characteristics of coal reservoirs,as they are factors affecting the reservoirs’gas production capacity and mechanical properties.Based on coal samples from the No.8 coal seam of the Carboniferous Benxi Formation in deep coalbed methane wells in the Ordos Basin,we investigate the developmental characteristics of macerals and microfractures using a residual neural network(ResNet).With 305 maceral and 65 microfracture sample points from the sampled coal,we develop a ResNet-based methodology for identifying macerals and microfractures in coals,and construct an identification and classification model for macerals and microfractures in deep coal reservoirs through the inversion of microscopically observed data using the ResNet technique.The results indicate that the model is reliable,as jointly corroborated by geological characteristics and clustering algorithm-derived results.The model demonstrates a prediction accuracy of 0.90 for macerals and 0.80 for microfractures,enabling the effective prediction of macerals and microfractures in coals.The identification and prediction results of the model reveal correlations between fracture morphologies and macerals.Notably,the fracture formation is the most closely correlated with vitrinites in macerals,with the predicted fracture types and numbers agreeing well with macerals.

关 键 词：分类模型 残差神经网络 显微组分 微裂缝 深部煤储层 煤层气 石炭系 鄂尔多斯盆地 

分 类 号：TE122.2[石油与天然气工程—油气勘探]