测井技术2025，Vol.49Issue(2)：218-225,8.DOI:10.16489/j.issn.1004-1338.2025.02.009
基于数据-算法双驱动融合的火成岩岩性识别方法
An Igneous Rock Lithology Identification Method Based on Data-Algorithm Bidirectional-Driven Framework
摘要
Abstract
Current igneous rock lithology identification technology faces compounded technical bottlenecks arising from significant disparities between conventional logging responses and lithological sensitivity,challenges in constructing continuous stratigraphic models using discrete mineral experimental data,and class imbalance in core-logging calibration samples.To address these challenges,this study proposes a Data-Algorithm Bidirectional-Driven Framework(DABDF)for igneous lithology identification.First,a feature space optimization algorithm constrained by discrete minerals is developed,integrating mineral chemical components with log data to establish a petrophysically meaningful feature characterization framework.Subsequently,a hierarchical resampling mechanism based on Mahalanobis distance is designed to mitigate recognition bias in minority sample classes.Finally,a probabilistically interpretable Bayesian deep forest model is constructed to achieve high-precision identification of complex lithologies.Validation experiments employing a nested verification strategy were conducted using 8 356 logging data from 20 wells in the eastern sag of the Liaohe basin.The proposed method achieved 100%accuracy in intra-well testing,89%accuracy in inter-well testing,and a weighted F1 score of 0.88,which demonstrated significant improvements over existing igneous rock lithology identification methods.This study shows that the deep integration of geological prior knowledge with deep learning effectively enhances the engineering applicability and interpretive reliability of lithology identification in igneous rock,and providing a novel technical solution for refined evaluation of complex reservoirs.关键词
测井解释/岩性识别/深度森林算法/测井数据融合/火成岩/样本不平衡处理Key words
log interpretation/lithology identification/deep forest algorithm/logging data fusion/igneous/class imbalance handling分类
天文与地球科学引用本文复制引用
韩锐羿,宋晓妮,王欣茹,郭宇航..基于数据-算法双驱动融合的火成岩岩性识别方法[J].测井技术,2025,49(2):218-225,8.基金项目
国家自然科学基金项目"基于T2-I模型的蚀变火山岩导电机理和饱和度模型研究"(42204122) （42204122）
