测井技术2025，Vol.49Issue(3)：329-336,8.DOI:10.16489/j.issn.1004-1338.2025.03.001
人工智能时代的地球物理测井:实践与展望
Geophysical Well Logging in the AI Era:Practices and Prospects
摘要
Abstract
Artificial intelligence(AI)technology has made significant progress,attracting considerable attention,and its applications are expanding across all industries.This article introduces the current state of AI applications in the oil and gas sector,analyzes their development trends,and presents preliminary progress in the exploration and research of AI application scenarios within the field of well logging.Constructing an effective intelligent well logging application scenario hinges on clearly defining the scenario's purpose,problem statement,business logic,input/output requirements,as well as the source and justification of the dataset and labels.The effectiveness of discriminative machine learning is fundamentally determined by the dataset and the labeling system.Well logging data possesses clear physical significance but is costly to acquire.It is influenced by factors such as the wellbore,invaded zone,surrounding rock,and the instrument's varying depths of investigation,vertical resolutions,and circumferential response characteristics.Meanwhile,core sample analysis and perforation/production testing are constrained by scale heterogeneity.These factors make effective point-by-point processing,interpretation,and labeling challenging.Consequently,generating well logging data via forward modeling becomes both necessary and feasible.The project team explored nine types of application scenarios:well log quality control,core depth matching,depth alignment,reservoir parameters prediction,cement bond evaluation,near-wellbore fracture identification,cased-hole logging evaluation,nuclear magnetic resonance(NMR)logging,and well log data generation.Scenario construction,model training,and testing have been completed.Among these,reservoir parameters prediction and cement bond evaluation have been deployed at scale and have yielded practical results.关键词
人工智能/地球物理/智能测井/机器学习/应用场景/构建方法/综述Key words
artificial intelligence(AI)/geophysics/intelligent logging/machine learning/application scenarios/construction methodology/review分类
天文与地球科学引用本文复制引用
肖立志..人工智能时代的地球物理测井:实践与展望[J].测井技术,2025,49(3):329-336,8.基金项目
中国石油天然气集团公司—中国石油大学(北京)战略合作项目"物探、测井、钻完井人工智能理论与应用场景关键技术研究"(ZLZX2020-03) （北京）
