礁相碳酸盐岩储层测井物性参数智能机器解释OA

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.