石油地质与工程2026，Vol.40Issue(1)：91-97,7.DOI:10.26976/j.cnki.sydz.202601013
礁相碳酸盐岩储层测井物性参数智能机器解释
Intelligent machine interpretation of logging physical property parameters for reef facies carbonatereservoirs:acase study of the second Member of Changxing Formation,Middle Permian in JN Gas Field
摘要
Abstract
Artificial intelligence(AI)has long been applied in the field of reservoir logging interpretation;however,its integrated application with multiple technologies remains scarce.Currently,the widespread adoption of cloud computing,big data,and AI is advancing the intelligent evolution of logging interpretation.To improve the exploration and development efficiency of reef gas reservoirs in the JN Gas Field,strengthening research on reservoir logging interpretation methods is crucial.Based on multi-source data,including core experimental analysis results and conventional logging data,this study conducts in-depth experimental investigations using advanced machine learning(ML)algorithms.By integrating models such as decision trees(DT)and gradient boosting decision trees(GBDT),high-accuracy regression prediction of porosity and permeability for reef-beach facies reservoirs is achieved.Experimental results of reservoir parameter logging interpretation demonstrate that ML methods significantly enhance the prediction accuracy of reservoir physical property parameters:the accuracy of porosity prediction increases from 0.362(via the acoustic-porosity crossplot method)to 0.908(via the DT algorithm),while the accuracy of permeability prediction rises from 0.009(via the porosity-permeability crossplot method)to 0.964(via the GBDT algorithm).In particular,these methods exhibit superior predictive performance in low-porosity and low-permeability carbonate reservoirs.The model proposed not only overcomes the limitations of traditional linear methods but also provides a new solution for the evaluation and development of complex reservoirs.It enhances the capability of logging in evaluating complex reservoirs and improves computational accuracy and efficiency.关键词
JN气田/长二段/礁相碳酸盐岩/储层物性解释/机器学习Key words
JN gas field/Chang 2Member/reef facies carbonate rock/reservoir physica property interpretation/machine learning分类
能源科技引用本文复制引用
康红,付晓飞,董卫,罗林波,荣焕青,谢润成,李思远,陈成..礁相碳酸盐岩储层测井物性参数智能机器解释[J].石油地质与工程,2026,40(1):91-97,7.基金项目
国家自然科学基金名称(41572130)及中石化科技部项目名称(P24155)资助. （41572130）
