Permeability and porosity are key parameters in reservoir characterization for understanding hydrocarbon flow behavior. While traditional laboratory core analysis is time-consuming, machine learning has emerged as a valuable tool for more efficient and accurate estimation. This paper proposes an ensemble technique called adaptive boosting (AdaBoost) for porosity and permeability estimation, utilizing methods such as support vector machine (SVM), Gaussian process regression (GPR), multivariate analysis, and backpropagation neural network (BPNN) for prediction based on well logs. Performance evaluation metrics including root mean square error, mean square error, and coefficient of determination (R²) were used to compare the models. The results demonstrate that AdaBoost outperformed GPR, SVM, and BPNN models in terms of processing time and accuracy, achieving R² values of 0.980 and 0.962 for permeability and porosity during training, respectively, and 0.960 and 0.951 during testing, respectively. This study highlights AdaBoost as a robust and accurate technique that can enhance reservoir characterization.

渗透率和孔隙度是储层表征中用于了解碳氢化合物流动行为的关键参数。虽然传统的实验室岩心分析非常耗时，但机器学习已成为一种更有效、更准确估计的宝贵工具。本文提出了一种称为自适应增强（AdaBoost）的集成技术，用于孔隙度和渗透率估计，利用支持向量机（SVM）、高斯过程回归（GPR）、多元分析和反向传播神经网络（BPNN）等方法进行预测测井日志。包括均方根误差、均方误差和决定系数 ( R ² ) 在内的性能评估指标用于比较模型。结果表明，AdaBoost 在处理时间和精度方面优于 GPR、SVM 和 BPNN 模型，训练期间渗透率和孔隙率的R ²值分别为 0.980 和 0.962，测试期间分别为 0.960 和 0.951。这项研究强调 AdaBoost 是一种强大而准确的技术，可以增强储层表征。