川南威远地区龙马溪组一段页岩有机碳含量测井预测模型优选  

Optimization of logging evaluation model for TOC content of shale in the first member of Longmaxi formation in Weiyuan area,south Sichuan

作　　者：李宜真 赵亮 张庆 刘子平 李俊翔 郝越翔 李勇[3] 吴朝容[3] 张兵[3] 耿茂宇 LI Yizhen;ZHAO Liang;ZHANG Qing;LIU Ziping;LI Junxiang;HAO Yuexiang;LI Yong;WU Chaorong;ZHANG Bing;GENG Maoyu(Shale Gas Exploration and Development Project Management Department of CNPC Chuanqing Drilling Engineering Company Limited,Chengdu 610051,China;Chengdu University of Technology,College of Energy,Chengdu 610059,China;Chengdu University of Technology,College of Geophysics,Chengdu 610059,China)

机构地区：[1]中国石油川庆钻探工程有限公司页岩气勘探开发项目经理部,成都610051 [2]成都理工大学能源学院,成都610059 [3]成都理工大学地球物理学院,成都610059

出　　处：《物探化探计算技术》2021年第5期598-608,共11页Computing Techniques For Geophysical and Geochemical Exploration

基　　金：中石油川庆钻探科研项目(CQCDLG-2020-02)。

摘　　要：在岩芯样品数量有限的情况下,无法获取连续的TOC数据时,可以使用测井曲线预测TOC含量。为验证不同测井预测方法在川南威远地区的适用性,基于X井中龙一段页岩的实测TOC含量数据及测井资料,分别用自然伽马能谱测井法、体积密度法、改进的ΔlgR法、多元测井参数回归分析法以及BP神经网络法建立TOC含量预测模型。使用三标度层次分析法在综合考虑模型参数数量、拟合程度及误差值影响的基础上,以总权重值代表综合评价结果,选出能兼顾多种优点的,精确度、实用性最高的预测模型。结果表明,BP神经网络模型得分最高,预测效果最好,是对威远地区X井龙一段TOC含量预测的最优模型,改进的ΔLgR模型次之。可将这两种模型应用于测井曲线预测TOC含量中,对于弥补样品数量有限、数据不足的问题具有一定的意义。When the number of core samples is limited and continuous TOC data cannot be available,logging curve can be used to predict TOC content.In order to verify the applicability of different logging prediction methods in Weiyuan area of south Sichuan,based on the measured TOC content data and logging data of the shale in the first member of Longmaxi formation in well X,this paper establishes the TOC content prediction model by using the natural gamma spectroscopy method,bulk density method,improvedΔlgR method,multiple linear regression logging methods and BP neural network method.Based on the comprehensive consideration of the influence of the number of model parameters,the degree of fitting and the error value,the Three-scale AHP method is used to select the prediction model with the highest accuracy and practicability that can take into account various advantages with the total weight value representing the comprehensive evaluation results.The results show that the BP neural network method has the highest score and the best prediction effect,which is the optimal method for the prediction of TOC content in the first member of Longmaxi formation in Weiyuan area,followed byΔLgR.These two methods can be applied to the prediction of TOC content in logging curves,which is of certain significance to make up for the limited number of samples and insufficient data.

关 键 词：页岩TOC含量 测井评价模型 三标度层次分析法 龙马组一段 威远地区 

分 类 号：P631.4[天文地球—地质矿产勘探]