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

• Grant number: 19/04886-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: June 01, 2019
• End date: May 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Agreement: Equinor (former Statoil)
• Principal Investigator: Anderson de Rezende Rocha
• Grantee: Victor Eduardo Martinez Abaunza
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Company: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica (FEM)
• 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.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MARTINEZ, VICTOR; ROCHA, ANDERSON. The Golem: A General Data-Driven Model for Oil & Gas Forecasting Based on Recurrent Neural Networks. IEEE ACCESS, v. 11, p. 28-pg., 2023-01-01. (19/04886-0, 17/15736-3)