古地理学报2025，Vol.27Issue(4)：903-923,21.DOI:10.7605/gdlxb.2025.090
碎屑岩储层智能表征与建模方法研究现状及展望
Advances and perspectives in intelligent characterization and modeling of clastic reservoirs
摘要
Abstract
Clastic rock reservoirs serve as critical carriers of hydrocarbon resources both in China and around the world.However,due to inherent limitations such as strong heterogeneity and insufficient subsurface characterization data,traditional methods of reservoir characterization and modeling have struggled to fulfill the demands for high-resolution exploration and efficient development.Since the 21th century,researchers have progressively integrated artificial intelligence(AI)techniques into the field of clastic reservoir characterization and modeling,resulting in significant advancements over the past decade.These innovations have significantly improved both the accuracy and efficiency of reservoir characterization.In this context,this paper systematically reviews the development history and current research status of intelligent technologies in clastic reservoir characterization and modeling.It highlights recent progress and application outcomes in areas such as intelligent well log interpretation for reservoir parameters,AI-based fault and stratigraphic framework analysis,intelligent reservoir prediction through well-seismic integration,and intelligent 3D geological modeling.Furthermore,we discuss the challenges faced by various intelligent approaches and outlines future directions for their development.Overall,these intelligent characterization techniques have made significant advances and demonstrated positive outcomes in practical applications.Nevertheless,they also face multiple challenges,including a lack of high-quality training samples,suboptimal generalization capabilities of learning models,and inadequate coupling of knowledge-driven with data-driven approaches.Despite these limitations,there remains significant potential for advancement,with promising application prospects emerging across reservoir characterization workflows.关键词
碎屑岩/储层表征/三维地质建模/测井解释/井震融合/人工智能Key words
clastic rock/reservoir characterization/3D geological modeling/well-log interpretation/well-seismic integration/artificial intelligence分类
天文与地球科学引用本文复制引用
岳大力,任柯宇,林津,张姝琪,李伟,王武荣,孙盼科,吴胜和,徐振华,刘磊,邬德刚,屈林博..碎屑岩储层智能表征与建模方法研究现状及展望[J].古地理学报,2025,27(4):903-923,21.基金项目
国家自然科学基金项目(编号:42272186,42202109,42302128,42412179)资助.[Financially supported by the National Natural Science Foundation of China(Nos.42272186,42202109,42302128,42412179)] （编号:42272186,42202109,42302128,42412179）
