Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses.

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models-Decision Tree, Bagging Tree, and Extra Trees-while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR).

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques.

The calorific value of any fuel is one of the crucial parameters to grade fuel's burning capability. The bomb calorimeter has historically been used to calculate coal's gross calorific value (GCV). However, for many years, engineers and scientists were trying to measure coal's GCV without a bomb calorimeter, using only laboratory-derived ultimate and/or proximate analyses to eliminate tedious and time-consuming laboratory analyses.