测井技术2024，Vol.48Issue(4)：416-427,12.DOI:10.16489/j.issn.1004-1338.2024.04.002
基于混合集成学习模型的测井曲线生成方法
Well Log Generation Based on Hybrid Ensemble Learning Model
摘要
Abstract
Well logs play a crucial role in reservoir evaluation and oil and gas resource assessment.However,the shortage issue of some well logs always exists in practical applications.The well log data are remeasured through a drilling procedure involving high cost and difficult implementation.To supplement the missing well logs without increasing economic costs,this study develops an approach for generating well logs based on the hybrid ensemble learning model.This approach efficiently and intelligently completes the missing well logging curves.The hybrid ensemble learning model combines the structural advantages of random forest model and extreme gradient boosting model,enabling deep exploration of the nonlinear mapping relationships among well log data and achieving accurate predictive results of log wells.The proposed hybrid ensemble learning model is applied to real well log data,and the generated results are compared with those of fully connected neural network model and multivariate linear regression model.Experimental results show that the well logs synthesized by the hybrid ensemble learning model exhibit higher accuracy,indicating the suitability of the hybrid ensemble learning model for well log generation.This study provides a new perspective for synthesizing artificial well logs.关键词
测井曲线/曲线生成/多元线性回归/全连接神经网络/混合集成学习Key words
well log/log generation/multivariate linear regression/fully connected neural network/hybrid ensemble learning分类
天文与地球科学引用本文复制引用
王宵宇,廖广志,肖立志,黄文松,孔祥文,赵子斌..基于混合集成学习模型的测井曲线生成方法[J].测井技术,2024,48(4):416-427,12.基金项目
国家自然科学基金"随钻核磁共振物化特性分析基础理论与探测方法研究"(51974337) （51974337）
