期刊文献+

基于自动超参数优化框架与梯度提升算法融合的储层粒度二维分布预测研究

Research on two-dimensional reservoir grain size distribution prediction based on the fusion of automatic hyperparameter optimization framework and gradient boosting algorithm
原文传递
导出
摘要 岩石粒度对水动力条件分析和沉积环境的识别具有指示性作用.传统粒度测量中常用的筛析法和激光法不仅耗时长、成本高,且由于钻井取芯收获率有限,导致粒度分布数据在深度上不连续.尽管采用测井曲线与机器学习结合的方法能够弥补岩石物理实验技术的不足,但现有成果均围绕粒度的一维特征值展开,无法表征二维粒度分布的整体特征.本文针对储层粒度二维分布曲线预测的困难,提出了基于自动超参数优化框架(Optuna)与梯度提升算法(LightGBM和XGBoost)融合的机器学习方法,以埕岛油田某区块测井数据和粒度分布实验数据为基础,通过对比线性回归、支持向量回归(SVR)、k-最近邻(k-NN)、随机森林(Random Forest)、梯度提升决策树(GBDT)、XGBoost、LightGBM、卷积神经网络(CNN)等8种不同的机器学习方法,优化机器学习参数,选出最适合于预测储层粒度分布的方法.研究结果表明:10种机器学习方法预测储层粒度分布的准确性有较大差异,当以自然电位、声波、井径、补偿中子、自然伽马、地层真电阻率、深侧向电阻率、微侧向电阻率和浅侧向电阻率9种测井参数数据作为输入时,本文提出的新方法对储层粒度二维分布预测精度最高,决定系数R2均接近0.7,误差较小,而线性回归、SVR、GBDT等储层粒度分布预测精度较低,不适用于储层粒度的预测. Rock grain size plays a significant role in the analysis of hydraulic conditions and the identification of depositional environments.Traditional methods for grain size measurement,for instance sieve analysis and laser diffraction,are time-consuming,costly,and suffer from discontinuity in depth due to limited core recovery during drilling.Although the combination of well log curves and machine learning methods can compensate for the limitations of rock physics experimental techniques,existing studies mainly focus on one-dimensional characteristic values of grain size,lacking a comprehensive representation of the two-dimensional grain size distribution.In this study,we propose a machine learning approach that combines the automatic hyperparameter optimization framework(Optuna)with gradient boosting algorithms(LightGBM and XGBoost)to address the challenge of predicting two-dimensional grain size distribution in reservoirs.Based on well log data and grain size distribution experimental data from a certain block in the Chengdao oilfield,we compare eight different machine learning methods,including linear regression,Support Vector Regression(SVR),k-Nearest Neighbors(k-NN),random forest,Gradient Boosting Decision Tree(GBDT),XGBoost,LightGBM,and Convolutional Neural Network(CNN).By optimizing the machine learning parameters,we identify the most appropriate method for predicting reservoir grain size distribution.The research results demonstrate significant differences in the accuracy of grain size distribution prediction among the ten machine learning methods.When using nine well log parameters,including natural potential,sonic,wellbore diameter,compensated neutron,natural gamma,formation resistivity,deep lateral resistivity,micro lateral resistivity,and shallow lateral resistivity,as inputs,the proposed method achieves the highest accuracy in predicting the two-dimensional grain size distribution in reservoirs,with R2 coefficients approaching 0.7 and smaller errors.Furthermore,linear regression,SVR,as well as GBDT attain lower accuracy in predicting reservoir grain size distribution,which are not eligible for grain size prediction in reservoirs.
作者 蒋熙梅 闫伟超 邢会林 孙建孟 JIANG XiMei;YAN WeiChao;XING HuiLin;SUN JianMeng(Frontiers Science Center for Deep Ocean Multispheres and Earth System,Key Lab of Submarine Geosciences and Prospecting Techniques,MOE and College of Marine Geosciences,Ocean University of China,Qingdao 266100,China;Deep Sea Multidisciplinary Research Center,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266237,China;College of Earth Sciences and Technology,China University of Petroleum(East China),Qingdao 266580,China)
出处 《地球物理学进展》 CSCD 北大核心 2024年第5期1886-1900,共15页 Progress in Geophysics
基金 国家自然科学基金(52074251,92058211) 山东省自然科学基金(ZR2020QD054)联合资助。
关键词 粒度分布 机器学习 Optuna XGBoost LightGBM Grain size distribution Machine learning Optuna XGBoost LightGBM
  • 相关文献

参考文献19

二级参考文献206

共引文献85

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部