石油地球物理勘探2025，Vol.60Issue(2)：273-282,10.DOI:10.13810/j.cnki.issn.1000-7210.20240138
基于卷积神经网络的页岩TOC三维定量预测方法
3D quantitative prediction method for shale TOC based on convolutional neural network
摘要
Abstract
The total organic carbon(TOC)content is an important evaluation index for shale gas exploration and development.Logging data can efficiently assess TOC,but it cannot be used for TOC prediction in inter-well areas.The TOC-sensitive factors extracted from seismic data can achieve three-dimensional(3D)predic-tion.However,due to the thin thickness and strong heterogeneity of shale reservoirs,it is difficult to achieve the required resolution by relying solely on seismic data.Therefore,it is necessary to comprehensively use mul-tiple data sources to improve the accuracy of TOC assessment.For this purpose,a high-precision quantitative prediction method for shale TOC based on a convolutional neural network(CNN)is proposed.Firstly,the cor-relation analysis between the measured TOC data of the core from drilling and multiple logging characteristic curves is conducted on the Longmaxi Formation shale in southern Sichuan,and the most representative and sen-sitive features are selected.Secondly,based on the identified sensitive parameters,a CNN prediction model is constructed.The measured TOC samples and the training samples constructed by sensitive logging parameters are divided into datasets at a ratio of 7:3 for model training and validation.Finally,the high-resolution sensitive parameter inversion results obtained by simulation of seismic waveform indication are used as the feature input for 3D TOC content prediction.The sensitive parameters are rearranged,reorganized,and then input into the CNN model to achieve 3D TOC content prediction.The research results show that CNN has more advantages than multiple regression and back propagation(BP)neural networks in fitting the nonlinear relationship be-tween TOC content and sensitive parameters.The average absolute error and root mean square error are both less than 0.6 between the predicted TOC data and the measured values from drilling.The prediction results are consistent with the actual situation.This method has high accuracy and obvious advantages in 3D TOC content prediction of thin shale reservoirs.关键词
TOC含量/敏感参数/卷积神经网络/波形指示模拟Key words
total organic carbon content/sensitive parameter/convolutional neural network/simulation of seis-mic waveform indication分类
天文与地球科学引用本文复制引用
汪子祺,吴朝容,黄开兴,孙正星,郝悦翔,李勇..基于卷积神经网络的页岩TOC三维定量预测方法[J].石油地球物理勘探,2025,60(2):273-282,10.基金项目
本项研究受中国石化地球物理重点实验室开放基金资助项目"基于AI的页岩储层参数定量预测方法研究"(36750000-23-FW0399-0005)资助. （36750000-23-FW0399-0005）
