石油科学通报2025，Vol.10Issue(5)：908-925,18.DOI:10.3969/j.issn.2096-1693.2025.01.022
非常规油气储层测井智能解释应用现状与发展趋势
Application status and development trends of artificial intelligence in logging interpretation for unconventional oil and gas reservoirs
摘要
Abstract
With the continuous advancement of oil and gas exploration technologies,unconventional hydrocarbon reservoirs have emerged as a pivotal domain for global energy resource augmentation and production enhancement.However,the inherent characteristics of low permeability,dense rock matrix,and complex heterogeneity in these reservoirs pose substantial challenges to conventional logging interpretation methodologies,particularly in constructing theoretical models,deriving empirical formulas,and inverting reservoir parameters,thereby hindering accurate reservoir identification and efficient development.The recent breakthroughs in artificial intelligence(AI)technologies have provided innovative solutions for logging interpretation in unconventional reservoirs.Through systematic analysis of cutting-edge research achievements worldwide,this paper first elucidates the core geological characteristics and evaluation challenges of unconventional reservoirs.Subsequently,it comprehensively summarizes the implementation modalities and operational efficacy of AI techniques,including machine learning and deep learning algorithms,in critical logging interpretation processes such as lithology identification,porosity prediction,permeability estimation,and hydrocarbon-bearing potential assessment.The study particularly highlights the transformative capabilities of convolutional neural networks in processing multi-scale logging data,recurrent neural networks in handling time-series measurements,and ensemble learning approaches in enhancing prediction accuracy under high-dimensional parameter spaces.The research demonstrates that AI-driven approaches achieve remarkable performance improvements compared to conventional methods,with reported accuracy enhancements of 25%～40%in lithofacies classification and 15%～30%reduction in mean absolute error for porosity estimation across various case studies.Furthermore,advanced deep learning architectures have shown exceptional capability in capturing nonlinear relationships between logging responses and reservoir properties,effectively addressing the"low signal-to-noise ratio"dilemma common in unconventional reservoir evaluation.A critical evaluation is conducted from multiple dimensions,including data quality requirements,algorithmic adaptability,computational efficiency,and model interpretability.The analysis reveals that while data-driven models excel in pattern recognition,their physical consistency and generalization capability require further improvement,particularly when dealing with spatially heterogeneous formations and limited training datasets.To address these challenges,the paper proposes three strategic development directions:(1)Hybrid modeling frameworks integrating physical constraints with data-driven approaches.(2)Transfer learning schemes for small-sample learning scenarios.(3)Multi-modal data fusion architectures incorporating logging,core,and seismic information.Moreover,the study emphasizes the necessity of establishing standardized workflows for feature engineering,model validation,and uncertainty quantification in AI-based logging interpretation systems.Special attention is given to emerging technologies such as graph neural networks for 3D reservoir characterization and physics-informed neural networks for incorporating petrophysical laws into machine learning architectures.This comprehensive review not only synthesizes the current state-of-the-art in intelligent logging interpretation but also provides a strategic roadmap for future research endeavors.The findings offer valuable theoretical references and methodological guidance for optimizing AI-based interpretation techniques in unconventional reservoir evaluation,ultimately contributing to more reliable reservoir characterization and enhanced hydrocarbon recovery in complex geological settings.关键词
非常规油气储层/人工智能/岩相预测/储层参数预测/甜点评价Key words
unconventional oil and gas reservoirs/artificial intelligence/lithofacies prediction/reservoir parameter prediction/sweet spot evaluation分类
能源科技引用本文复制引用
罗功伟,安小平,姚卫华,邹永玲..非常规油气储层测井智能解释应用现状与发展趋势[J].石油科学通报,2025,10(5):908-925,18.基金项目
中石油集团公司攻关性应用性科技专项"陆相页岩油规模增储上产与勘探开发技术研究"(2023ZZ15YJ07)和中国石油股份公司科技项目"油气勘探开发人工智能关键技术研究"(2023DJ84)联合资助 （2023ZZ15YJ07）
