地质科技通报2025，Vol.44Issue(4)：2-15,14.DOI:10.19509/j.cnki.dzkq.tb20240539
基于机器学习测井反演的煤体结构评价:以鄂尔多斯盆地榆林地区本溪组8号煤为例
Evaluation of coal structure based on machine learning logging inversion:A case from No.8 coal of Benxi Formation in Yulin area of Ordos Basin
摘要
Abstract
[Objective]Coal structure directly affects the pore and fracture system of coal reservoirs.Therefore,the accurate identification of coal structure is crucial for guiding coal seam fracturing and coal bed methane extraction.Taking No.8 coal of the Benxi Formation in the Yulin area of the Ordos Basin as an example,the complex coal structure necessitates the introduction of machine learning methods to address the nonlinear challenges in logging data interpretation.[Methods]In this study,Back Propagation(BP)neural network,Random Forest,and XGBoost algorithms are used to train on pre-processed core well data from the study area to invert coal structure across this region.By integrating the top and bottom plates of the coal seams and the coal thickness,we explore the development of coal structure under tectonic control.[Results]The results indicate that:(1)Compared to the BP neural network,Random Forest and XGBoost algorithms provide more accurate inversion results,aligning more closely with real core observations.(2)The degree of coal structure fragmentation in No.8 coal in the Yulin area increases progressively from northwest to southeast.(3)Tectonic zones,developed from the central to southeastern part of the study area,cause a decrease in coal thickness,with the coal structure transitioning from primary coal to mylonitic coal under tectonic influences.[Conclusion]The three machine learning algorithms employed in this study successfully inverted the complex coal structure,with Random Forest and XGBoost achieving higher inversion accuracy.Additionally,the relationship between coal structural variations and the development of tectonic zones was analyzed,providing valuable insights for identifying coal structures and evaluating tectonic zones in coalbed methane production.关键词
煤体结构/机器学习/BP神经网络/随机森林/XGBoost/构造控制/鄂尔多斯盆地/测井Key words
coal structure/machine learning/Back Propagation neural network/Random Forest/XGBoost/structural control/Ordos Basin/logging分类
能源科技引用本文复制引用
李安,蔡益栋,王子豪,刘大锰..基于机器学习测井反演的煤体结构评价:以鄂尔多斯盆地榆林地区本溪组8号煤为例[J].地质科技通报,2025,44(4):2-15,14.基金项目
国家自然科学基金项目(42130806 （）
42372195) （）
中央高校基本科研业务费深时数字地球前沿科学中心"深时数字地球"中央高校科技领军人才团队项目(2652023001) （2652023001）
