Porosity and Density Determination from Well Log Data: Machine Learning and Simulation Approaches

Abstract

Reservoir characterization is vital for petroleum exploration, largely relies on well log data analysis. Machine Learning (ML) empowers analysis of complex datasets quickly and more easily in a cost-effective way. ML allows deeper insights into reservoir properties such as porosity, permeability, water saturation, resistivity and many more. This study focuses on Reservoir characterization using ML approach (Python). Investigating reservoir behaviours involves intricate inverse problems; ML tackles this challenge. Integration of ML improves understanding, optimizing petroleum industry practices. This study used data from well logs to evaluate porosity, density, and gamma ray log. Through Petrel-based simulations, the findings were verified and validated.