A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran.

ABSTRACT: Petrophysical rock typing (PRT) and permeability prediction are of great significance for various disciplines of oil and gas industry. This study offers a novel, explainable data-driven approach to enhance the accuracy of petrophysical rock typing via a combination of supervised and unsupervised machine learning methods. 128 core data, including porosity, permeability, connate water saturation (S_wc), and radius of pore throats at 35% mercury injection (R₃₅) were obtained from a heterogeneous carbonate reservoir in Iran and used to train a supervised machine learning algorithm called Extreme Gradient Boosting (XGB). The algorithm output was a modified formation zone index (FZIM*), which was used to accurately estimate permeability (R² = 0.97) and R₃₅

SUBMITTER: Mohammadian E 

PROVIDER: S-EPMC8927145 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature