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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


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.