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