STUDY OF PERMEABILITY PREDICTION USING HYDRAULIC FLOW UNIT (HFU) AND MACHINE LEARNING METHOD IN “BSH” FIELD

Abstract

In this study, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines, and K-Nearest Neighbor Machine Learning model are presented that use log and core data available as the basis for permeability prediction. The results were then compared to previously available method, namely Hydraulic Flow Unit (HFU) based on MAE and RMSE Value. The approach was taken by considering correlation relationships between existing log data in predicting permeability values. Three correlations, namely Spearman, Pearson, and Kendall, will be used to determine the relationship between existing log data and permeability. The machine learning model is then compared with the Hydraulic Flow Unit (HFU) Method in predicting the permeability value. The Novelty of this Machine Learning Model is to be able to predict permeability value, to solve the problem of accuracy using the existing method, and to save reasonable time to obtain permeability value by coring in the laboratory by utilizing standard computer available.