Convex metamodels for reservoir optimization

• Grant number: 22/04255-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2022
• End date: April 30, 2023
• Field of knowledge: Engineering - Mechanical Engineering
• Agreement: Equinor (former Statoil)
• Principal Investigator: Denis José Schiozer
• Grantee: Eduardo Guimarães Lino de Paula
• Host Institution: Faculdade de Engenharia Mecânica (FEM). 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

A metamodel, or surrogate model, is a simplified model of an actual model of a system. Metamodels are particularly important when the true model is expensive to run, as they typically map a set of inputs to an expected output through regressions, which are, in general, quick to evaluate. It is possible to consider local metamodels, where the validity of the results is expected only in the vicinity of some point, or global metamodels, which define a relationship that is valid across all feasible input values. In a variety of applications, many regression methods have been used to fit global metamodels, including radial basis functions, gaussian processes, stochastic kriging, and neural networks. Metamodel optimization is used for inferring optimal decisions from observational data generated by a black-box simulator. We propose hereto evaluate the quality of the main convex metamodels in use now in the UNISIM research group. (AU)