Perpustakaan Digital ITB

In oil and gas reservoirs, understanding how multiple fluids flow simultaneously is crucial for optimizing production, and relative permeability is the key parameter governing this complex process. Traditional barriers of resource-intensive and costly core flooding experiments have motivated our study to overcome these challenges, using machine learning models for a comprehensive understanding of fluid behavior within reservoirs. This research aims to explore the potential of machine learning to enhance the accuracy of oil and water relative permeability prediction.. This study utilizes data from core samples representing different lithologies, specifically sandstone from the Talang Akar Formation and carbonate from the Baturaja Formation, collected from three oil fields in Indonesia. This initial dataset, comprised of 519 data points, was obtained after removing data points exhibiting the impact of fine migration to ensure the accuracy of the machine learning models. This study introduces the use of hybrid machine learning models to predict oil and water relative permeability curves without relying on absolute permeability as an input feature. Altough absolute permeability has been shown to be strongly correlate ted with relative permeability in previous studies, this research deliberately excludes it to explore alternative predictive parameters. By utilizing readily available data, including water saturation, irreducible water saturation, residual oil saturation, and porosity, our models achieve accurate and rapid predictions of relative permeability. This novel approach has the potential to make reservoir characterization easier and reduce the need for time-consuming and expensive special core analysis. Furthermore, our study explores the potential of these models to correct core sample data affected by fine migration, a common challenge in special core analysis. This finding offers a promising approach for improving the reliability of relative permeability data, particularly in cases where fine migration is an important consideration.