Type
Supervisor
Subject
Date
2026-03-09
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Traditional mathematical models used to simulate hydrocarbon reservoirs are often complex and time-consuming. Parallel computing can reduce some of these limitations, but an alternative solution is machine learning, which has shown strong potential for prediction tasks across various fields. By training on historical oil field data and outputs from conventional simulators, machine learning models can offer faster and more efficient reservoir simulations.
Department
Publisher
Sponsor
None
Copyright
Book title
Numerical Methods in Porous Media MATLAB® and Python Approaches