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This field case study aims to address the challenge of accurately predictingpetrophysical properties in heterogeneous reservoir formations, which cansignificantly impact reservoir performance predictions. The study employedthree machine learning algorithms, namely Artificial Neural Network (ANN),Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predictpermeability log from conventional logs and match it with core data. Theprimary objective of this study was to compare the effectiveness of the threemachine learning algorithms in predicting permeability and determine theoptimal 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 futurewells more accurately. The study concluded that the FZI approach and machinelearning algorithms are effective in predicting permeability log and improvingreservoir performance predictions.

Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs