This field case study aims to address the challenge of accurately predicting petrophysical properties in heterogeneous reservoir formations, which can significantly impact reservoir performance predictions. The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predict permeability log from conventional logs and match it with core data. The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The study utilized the Flow Zone Indicator (FZI) rock typing technique to understand the factors influencing reservoir quality. The findings will be used to improve reservoir simulation and locate future wells more accurately. The study concluded that the FZI approach and machine learning algorithms are effective in predicting permeability log and improving reservoir performance predictions.

使用人工神经网络、随机森林分类器和支持向量机等三种模型，以预测渗透率为主要目标，比较了三种机器学习算法的有效性，并利用FZI岩性分类技术对影响油藏质量的因素进行了分析，表明FZI模型和机器学习算法在预测渗透率和提高油藏性能方面具有一定的实际应用价值。

利用机器学习增强岩石物理研究：异质储层渗透率预测现场案例研究