LogDiffusion:一种基于扩散概率模型的岩性识别方法  

LogDiffusion:a method of lithology identification based on diffusion probability model

作　　者：赵逢达[1,2,3] 韩滋民 付晓飞 章蓬伟[1,2] 李贤善 ZHAO FengDa;HAN ZiMin;FU XiaoFei;ZHANG PengWei;LI XianShan(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;School of Information Science and Engineering,Xinjiang University of Science and Technology,Korla 841000,China;The Key Laboratory for Software Engineering of Hebei Province,Yanshan University,Qinhuangdao 066004,China;Key Laboratory of“Continental Shale Oil and Gas Reservoir Formation and Efficient Development”,Northeast Petroleum University,Daqing 163319,China)

机构地区：[1]燕山大学信息科学与工程学院,秦皇岛066004 [2]新疆科技学院信息科学与工程学院,库尔勒841000 [3]燕山大学河北省软件工程重点实验室,秦皇岛066004 [4]东北石油大学“陆相页岩油气成藏及高效开发”教育部重点实验室,大庆163319

出　　处：《地球物理学进展》2025年第1期106-120,共15页Progress in Geophysics

基　　金：国家自然科学基金(U20A2093);新疆维吾尔自治区自然科学基金面上项目(2022D01A59);中央引导地方科技发展资金项目(246Z1817G);河北省创新能力提升计划项目(22567637H)联合资助.

摘　　要：岩性识别是油气资源勘查开发过程中的关键步骤之一.目前,利用深度学习技术进行测井岩性识别能够显著提高识别速度和准确率,然而,由于测井数据集经常存在数据量不足和岩性类别分布不均衡等问题,神经网络在训练过程中容易出现过拟合现象,导致模型的准确率降低.为了解决这些问题,本文提出一种基于扩散概率模型的岩性识别模型LogDiffusion,该模型能够生成高质量的测井数据并用于训练,从而提升岩性识别的分类准确率.在传统的扩散概率模型的基础上,考虑到测井数据的一维结构,本文设计了一种用于估计梯度的分数网络FT-Unet,并提出了一种辅助分类器FT-Transformer以获取准确的岩性标签.此外,还提出了一种基于阈值的动态标签机制以提高采样算法的准确性.在两个小样本盲井测井数据集上的实验结果表明,该方法能够一定程度上解决测井数据集数据量不足和岩性类别分布不均衡的问题,从而提升岩性识别的准确率和精度.Lithology identification is one of the key steps in the exploration and development of oil and gas resources.At present,using deep learning technology to identify lithology in logging can significantly improve the identification speed and accuracy.However,due to the shortage of data in logging data sets and the uneven distribution of lithology categories,the neural network is prone to overfitting in the training process,resulting in a decrease in the accuracy of the model.In order to solve these problems,a lithology identification model LogDiffusion based on diffusion probability model is proposed in this paper,which can generate high quality logging data and be used for training,so as to improve the classification accuracy of lithology identification.Based on the traditional diffusion probability model and considering the one-dimensional structure of log data,a fractional network FT-Unet for gradient estimation is designed in this paper,and an auxiliary classifier FT-Transformer is proposed to obtain accurate lithology labels.In addition,a threshold based dynamic labeling mechanism is proposed to improve the accuracy of the sampling algorithm.The experimental results on two small-sample blind well logging data sets show that this method can alleviate the problems of insufficient data quantity and uneven distribution of lithology categories in the logging data set to a certain extent,so as to improve the accuracy and precision of lithology identification.

关 键 词：岩性识别 数据增强 深度学习 扩散概率模型 

分 类 号：P631[天文地球—地质矿产勘探]