基于深度学习的FMI测井图像裂缝分割研究  

Research on fracture segmentation of FMI logging images based on deep learning

作　　者：陈玙璠 王杨[1] 蒋薇 王永生 梅青燕 王欣 CHEN YuFan;WANG Yang;JIANG Wei;WANG YongSheng;MEI QingYan;WANG Xin(School of Computer Science,Southwest Petroleum University,Chengdu 610500,China;Exploration and Development Research Institute,PetroChina Southwest Oil and Gasfield Company,Chengdu 610043,China)

机构地区：[1]西南石油大学计算机科学学院,成都610500 [2]中国石油西南油气田勘探开发研究院,成都610043

出　　处：《地球物理学进展》2025年第1期143-154,共12页Progress in Geophysics

基　　金：油气藏地质及开发工程国家重点实验室开放性研究课题(PLN 2022-33);南充市市校科技战略合作项目(23XNSYSX0111)联合资助.

摘　　要：在储层钻探开发过程中,准确提取、识别与评价地层中的裂缝对指导油气勘探的钻探和开发具有重要意义.针对传统方法对裂缝区域分割不精细的问题,提出一种基于深度学习的FMI(Formation Micro-Scanner Image)测井图像裂缝分割方法.首先,利用F-Criminisi算法对原始FMI测井图像中缺失像素信息的空白条带进行图像修复.接着构建以U-Net为基础的生成对抗网络,并引入双重注意力机制,构建裂缝分割模型,实现复杂背景下对裂缝的精确分割,结合像素与边缘信息设计损失函数,使模型更准确分割测井图像中的裂缝与背景区域,使分割结果中的裂缝边界更清晰.本文利用实测碳酸盐岩储层FMI测井图像对提出的模型进行了测试.结果表明,所提裂缝分割方法的Dice系数相较于经典的分割模型U-Net提升5%.该方法能够精确地提取FMI测井图像中的裂缝信息,为后续裂缝参数的定量计算和测井解译提供了基础,具备较强的实用性.In the process of reservoir drilling and development,it is of great significance to accurately extract,identify and evaluate the fractures in the formation to guide the drilling and development of oil and gas exploration.To solve the problem of imprecise fracture region segmentation by traditional methods,a fracture segmentation method based on Formation Micro-Scanner Image based on deep learning is proposed.Firstly,F-Criminisi algorithm is used to repair the blank strip with missing pixel information in the original FMI logging image.Then,a generative adversus-network based on U-Net is constructed,and dual attention mechanism is introduced to construct a fracture segmentation model to achieve accurate fracture segmentation under complex background.Combining pixel and edge information,loss function is designed to enable the model to more accurately segment the fracture and background region in the logging image and make the fracture boundary in the segmentation result clearer.In this paper,the proposed model is tested by using real FMI logging image of carbonate reservoir.The results show that the Dice coefficient of the proposed fracture segmentation method is 5%higher than that of the classical fracture segmentation model U-Net.This method can accurately extract fracture information from FMI logging images,and provides a basis for subsequent quantitative calculation of fracture parameters and logging interpretation,and has good practicability.

关 键 词：裂缝分割 深度学习 语义分割 生成对抗网络 成像测井 注意力机制 

分 类 号：P631[天文地球—地质矿产勘探]