测井技术2026，Vol.50Issue(3)：416-426,11.DOI:10.16489/j.issn.1004-1338.2026.03.004
融合多模态特征与深度学习的沉积单元界限智能对比方法
Intelligent Correlation Method for Sedimentary Unit Boundaries Integrating Multimodal Features and Deep Reinforcement Learning
摘要
Abstract
To improve the automation,objectivity,and engineering efficiency of sedimentary unit boundary correlation in the Sanan development area of the Daqing oilfield,where dense well patterns and highly subdivided stratigraphic sequences make manual correlation inefficient,subjective,and inconsistent in complex intervals,an intelligent correlation method integrating multimodal features and deep reinforcement learning is developed.Using SP,GR,RLLD,and AC logging curves together with their roughness attributes,this study investigated collaborative representation of multimodal logging information,optimization of inter-well boundary matching paths,and automatic identification mechanisms for complex intervals.First,multi-source logging curves are synchronously sampled,standardized,smoothed,and organized into sliding-window inputs.Then,convolutional feature extraction,bidirectional long short-term memory(BiLSTM)temporal modeling,and attention-based adaptive fusion are employed to obtain integrated features for boundary identification.Furthermore,curve-roughness similarity measurement,a variable-window search strategy,and deep Q-network(DQN)-based sequential decision-making are combined to achieve automatic boundary matching and global path optimization between reference wells and target wells.The results show that,based on measured data from 1 500 wells in 23 blocks,the proposed method achieved an overall matching rate of 94.1%on 1 350 test wells,with an average processing time of approximately 52 s per well.Among 105 000 sedimentary unit boundary samples,78.1%of the absolute boundary-depth errors are controlled within 0.3 m,89.5%within 0.5 m,and 96.2%within 0.7 m.The comprehensive identification accuracy for six major marker beds is about 94.3%;among them,four marker beds achieved accuracies higher than 95%,while the remaining two reached about 91%～93%.Compared with the traditional correlation coefficient plus gradient method,as well as convolutional neural network(CNN)-BiLSTM and graph neural network(GNN)models,the proposed method outperforms comparative methods across all metrics including stratified identification accuracy,F1 score,intersection over union(IoU),overall matching rate,and processing efficiency.It is concluded that this method can effectively improve the accuracy,stability,and engineering applicability of sedimentary unit boundary correlation under large-scale well-network conditions,and can provide reliable technical support for fine stratigraphic correlation,reservoir characterization,and subsequent geological modeling.关键词
精细地层对比/测井统层/沉积单元界限/井间层位对比/多模态特征融合/深度强化学习/层界自动匹配/全局路径优化Key words
fine stratigraphic correlation/well-log stratigraphic correlation/sedimentary unit boundary/inter-well horizon correlation/multimodal feature fusion/deep reinforcement learning/automatic boundary matching/global path optimization分类
天文与地球科学引用本文复制引用
李全厚,黄俊杰,方涛,郑泽伟..融合多模态特征与深度学习的沉积单元界限智能对比方法[J].测井技术,2026,50(3):416-426,11.基金项目
国家科技重大专项课题"整装油藏变流线智能立体井网重构及注采调控优化技术"(2025ZD1406102) （2025ZD1406102）
国家自然科学基金青年科学基金项目"陆相页岩游离油含量及可动性定量评价研究"(42102200) （42102200）
黑龙江省自然科学基金联合引导面上项目"基于马尔科夫链的厚度随机分布薄互层时频响应机理研究"(LH2021D010) （LH2021D010）
