Machine-learning and data-driven techniques for oil recovery in the context of reservoir control and management

• Grant number: 19/04886-0
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): June 01, 2019
• Effective date (End): May 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Cooperation agreement: Equinor (former Statoil)
• Principal Investigator: Anderson de Rezende Rocha
• Grantee: Victor Eduardo Martinez Abaunza
• Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 17/15736-3 - Engineering Research Centre in Reservoir and Production Management, AP.PCPE

Abstract

In this project, we will leverage reservoir control and management (as example, the use of WAG injectionand/or advanced well control) methods to induce producing more oil production in oil field. Each oftechniques to be applied may have very different operations and, in this research, we will develop theappropriate machine-learning methods to evaluate data associate with each one of them to point out theirapplicability, pros and cons when targeting improving oil production in oil fields.