测井技术2025，Vol.49Issue(1)：47-56,87,11.DOI:10.16489/j.issn.1004-1338.2025.01.006
基于增强域数据微调Yolo模型的储气库断层智能识别方法及应用
Enhanced Domain Tuned Yolo-Driven Intelligent Fault Identification Method:Application in Selection and Construction of Gas Storage
摘要
Abstract
As an essential channel for the accumulation and migration of oil and gas in reservoirs,geological faults are significant indicators for evaluating reservoir characteristics and trap closure,and they are also a prerequisite for the selection of structural styles in gas storage reservoirs.Nevertheless,identifying faults from seismic image data heavily relies on expert knowledge,suffers from poor timeliness,and has strong ambiguity.In recent years,artificial intelligence methods represented by deep learning and large-scale model technologies have profoundly transformed the paradigms of traditional industrial tasks with their highly efficient nonlinear data analysis capabilities.Based on this,this paper proposes an intelligent fault identification method based on the enhanced domain data fine-tuning of the Yolo model.Firstly,to address the sparse onsite data,an image self-enhancement algorithm based on reinforcement learning is employed.Through the downstream task requirements-directed training and optimization algorithm,the optimal enhancement combination scheme of seismic volume images is achieved.Then,in accordance with the expert knowledge in the geological domain,the high-order features that can effectively represent fault blocks are determined in three-dimensional seismic images.By further establishing a fault identification model based on the pre-trained Yolo model and inputting the measured and enhanced image data for domain data fine-tuning training,the intelligent fault identification model is established.Finally,the on-site three-dimensional seismic data is input into the trained intelligent fault identification model to extract the fault features that have been segmented,identified,labeled,and calculated.This technique can effectively detect formation faults without heavily depending on human interaction,as demonstrated by the building and operation plot of a gas storage facility in central China.This study can offer clever solutions for the sensible placement of gas storage facilities and is relevant to the task of seismic exploration fault identification.关键词
三维地震勘探/断层识别/深度学习/Yolo模型/储气库Key words
3-D seismic exploration/fault identification/deep learning/Yolo model/gas storage引用本文复制引用
白雪峰,张峰源,邹环宇,黄发木,李俊伦,赵世杰,张莉,汤继周..基于增强域数据微调Yolo模型的储气库断层智能识别方法及应用[J].测井技术,2025,49(1):47-56,87,11.基金项目
国家自然科学基金项目"长宁页岩气开发区地震活动性实时监测与机理研究"(U2139204) （U2139204）
上海市"科技创新行动计划"启明星项目"超深层油气资源开采与二氧化碳协同封存多场多相耦合机制研究"(24QA2709700) （24QA2709700）
中国石油科技创新基金项目"多模式压裂裂缝竞争扩展机理与多相态耦合效应研究"(2021 DQ02-0501) （2021 DQ02-0501）
