天然气勘探与开发2025，Vol.48Issue(4)：116-125,10.DOI:10.12055/gaskk.issn.1673-3177.2025.04.012
基于LightGBM的咸水中CO2溶解度预测方法
A LightGBM-based method for predicting CO2 solubility in brine
摘要
Abstract
To address the problems in the existing methods for predicting CO2 solubility in brine such as complex process,narrow applicability,and low accuracy,a modeling method based on machine learning technology termed"weight reconstruction+secondary training+deep optimization"was proposed to construct a LightGBM-based prediction model.Firstly,experimental data of CO2 solubility in brine were extensively investigated according to the general situations of saline aquifers of major basins in China.Secondly,a LightGBM-based prediction model was preliminarily established using the TPE algorithm and five-fold cross validation.In addition,the model was deeply optimized with respect to the decision tree structure and out-of-bag sampling frequency using grid search method.Finally,the new model was employed to analyze the variation laws of CO2 solubility,after its comprehensive performance was evaluated through various indicators.The results show that the newly established CO2 solubility prediction model has high prediction accuracy and reliability,strong generalization ability,with a mean square error of 0.00089(mol/kg)2,a mean absolute percentage error of 3.78%,and a determination coefficient of 0.994,outperforming conventional models such as Duan&Sun,KRR,RBFNN-BAC and SVM.The CO2 solubility in brine is most affected by temperature,followed by pressure,and least by ion concentration.Moreover,the variation of CO2 solubility with temperature reverses at the pressure of 20 MPa.It is concluded that the research results can provide a basis for evaluating the potential of CO2 sequestration in saline aquifers and selecting suitable sequestration sites.关键词
CO2溶解度/预测模型/机器学习/LightGBM/深度优化/咸水层/碳封存Key words
CO2 solubility/Prediction model/Machine learning/LightGBM/Deep optimization/Saline aquifer/Carbon sequestration分类
能源科技引用本文复制引用
张卫,戚会清,陈刚,赵安琪,胡世莱..基于LightGBM的咸水中CO2溶解度预测方法[J].天然气勘探与开发,2025,48(4):116-125,10.基金项目
重庆市教委科学技术研究项目"储气库多周期强注采下气—水两相渗流规律及近井区域盐析—运移—堵塞机制"(编号:KJQN202401501). （编号:KJQN202401501）
