Machine learning-based prediction of porosity in tight sandstone reservoirs for CO2 geological sequestration
CO2 geological storage technology is a crucial measure in reducing atmospheric CO2 concentration and is considered as one of the key technologies for achieving China's"dual carbon"goals.Porosity,as a key parameter for evaluating the reservoir storage performance,plays a vital role in CO2 storage potential evaluation.However,in the case of CO2 storage in tight sandstone reservoirs,due to the diverse pore types and strong heterogeneity,the predicted results using existing models and logging interpretation methods often deviate greatly from the actual test results,resulting in poor actual application effects.Therefore,the establishment of a porosity model with high prediction accuracy and strong generalization performance by using cutting-edge mathematical algorithms to fully explore the porosity information hidden in logging data is crucial for the fine exploration and efficient development of tight sandstone reservoirs,as well as the evaluation of CO2 geological storage potential.This paper presents a porosity prediction model for tight sandstone reservoirs based on machine learning techniques,including Principal Component Regression(PCR),Gaussian Process Regression(GPR),Random Forest(RF),Support Vector Machine(SVM),BP Neural Network(BP-ANN),and Extreme Gradient Boosting Algorithm(XGBoost).Through comprehensive comparison,the XGBoost-based porosity prediction model is found to have the highest prediction accuracy and strongest generalization performance,providing new ideas for the construction of porosity prediction models for tight sandstone reservoirs.
CO2 geological storageTight sandstone reservoirPorosity modelMachine learningXGBoost
NETL
NSTL
万方数据
