摘要
人工智能技术已取得长足进展,引发极大关注,其应用在各行各业全面展开。该文介绍油气领域人工智能应用现状,分析其发展趋势,展示测井领域人工智能应用场景探索研究的初步进展。构建一个有效的智能测井应用场景,关键是要明确场景的目的、问题与业务逻辑,输入、输出要求,数据集和标签的来源及其合理性。判别式机器学习的应用效果主要由数据集和标签体系决定。测井数据物理意义清晰,获取成本昂贵,受到井筒、侵入带、围岩及仪器不同探测深度、不同纵向分辨率、不同周向响应特征等因素影响,而取样岩心分析和射孔求产受制于尺度非均质性,使逐点处理解释有效标注存在困难,正演模拟生成测井数据成为必要且具有一定可行性。研究团队探索了测井质量控制、岩心归位、深度对齐、储层参数预测、固井质量评价、井周裂缝识别、套后测井评价、核磁共振测井、测井数据生成等9类场景,完成场景构建和模型训练及测试,储层参数预测和固井质量评价已规模部署并取得应用成效。
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.
作者
肖立志
XIAO Lizhi(College of Geophysics,China University of Petroleum,Beijing 102249,China)
出处
《测井技术》
2025年第3期329-336,共8页
Well Logging Technology
基金
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)。
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
artificial intelligence(AI)
geophysics
intelligent logging
machine learning
application scenarios
construction methodology
review
作者简介
第一作者:肖立志,男,1962年生,博士,教授,从事地球物理测井、核磁共振、人工智能的教育教学和科学研究工作。E-mail:xiaolizhi@cup.edu.cn。
