摘要
南华北盆地西北缘主力层段山西组—太原组发育多期次断裂.该区非均质性较强、裂隙形成机制复杂、小尺度隐伏断裂比较发育,利用常规预测手段获得的小尺度裂缝边界特征模糊,精度较低,严重制约了深部煤系气的开发进程.因此,亟需寻找一种适合于研究区的隐伏构造预测方法.本文以JF1井区为例,提出融合灰度共生矩阵的纹理分析和Mini Batch K-means深度聚类的隐伏断裂识别方法.首先,本文采用时变分频反褶积技术进行了拓频处理,获取了宽频带的叠后地震数据.然后,通过优化三维滑动窗口尺度和灰度级数,在滑动窗口按照指定的真倾角和方位角生成灰度共生矩阵,分别提取熵、差异性、均一性及能量的纹理特征.将基于真倾角、方位角约束的纹理属性作为样本数据,设定Mini Batch K-means初始聚类中心,优化小批量数据子集,建立适合JF1井区的Mini Batch Kmeans深度学习模型.最后,基于智能蚁群算法优选裂缝优势路径,结合连续裂缝网格的粗化网格,构建小尺度连续缝网模型,可以获得局部隐伏断裂系统的连通性规律.结果表明,小批量、分批次的Mini Batch Kmeans算法对隐伏断裂系统分析具有重要作用.Mini Batch K-means深度学习模型和小尺度连通性预测方法对南华北盆地JF1井区隐伏断裂发育区的预测具有较好的应用效果.
Multi-stage faults are developed in the Shanxi Formation-Taiyuan Formation,the main strata in the northwestern margin of the South North China Basin.In this area,the heterogeneity is strong,the fracture formation mechanism is complex,and the small-scale hidden faults are relatively developed.The small-scale fracture boundary characteristics obtained by conventional prediction methods are fuzzy and the accuracy is low,which seriously restricts the development process of deep coal measure gas.Therefore,it is urgent to find a concealed structure prediction method suitable for the study area.Taking JF1 well area as an example,this paper proposes a method of hidden fracture identification based on texture analysis of gray level co-occurrence matrix and Mini Batch K-means deep clustering.Firstly,this paper uses time-varying frequency division deconvolution technology to carry out frequency expansion processing,and obtains broadband post-stack seismic data.Then,by optimizing the scale and gray level of the three-dimensional sliding window,the gray level co-occurrence matrix is generated in the sliding window according to the specified true dip angle and azimuth angle,and the texture features of entropy,difference,uniformity and energy are extracted respectively.The texture attributes based on true dip angle and azimuth angle constraints are used as sample data.The initial clustering center of Mini Batch K-means is set,and the small batch data subset is optimized to establish a Mini Batch K-means deep learning model suitable for JF1 well area.Finally,based on intelligent ant colony algorithm optimization.
作者
许军
李丛
张栋
张垚垚
袁青松
董果果
张馨元
瓮纪昌
刘炎昊
XU Jun;LI Cong;ZHANG Dong;ZHANG YaoYao;YUAN QingSong;DONG GuoGuo;ZHANG XinYuan;WENG JiChang;LIU YanHao(Henan Institude of Geology,Zhengzhou 450001,China;Clean Energy Industry Technology Research Institute,Shangqiu 476000,China;Underground Clean Energy Exploration and Development Industry Technology Innovation Strategic Alliance,Zhengzhou 450001,China;Chinese Academy of Geological Sciences,Beijing 100037,China)
出处
《地球物理学进展》
北大核心
2025年第5期2014-2027,共14页
Progress in Geophysics
基金
2024年河南省地质研究院科研项目“深部煤系气富集条件及甜点区预测研究”(2024-331-XM11)
河南省2025年度科技攻关项目“基于岩屑图像识别的钻头磨损评价方法研究”(252102320323)
中国地质调查局油气资源调查中心科研项目“河南地区煤系气地质条件与资源评价参数调查”(2024102)
2025年河南省地质研究院科研项目“豫东深部煤系气孔缝网络及差异赋存机理”(2025-904-XM02)
“基于深度学习的薄储层含气特征地震分析方法原理——以豫东地区为例”(2025-904-XM01)联合资助。
