Investigation of Advanced Techniques to Represent in Simulation Models Carbonate Oil Reservoirs with High Gas Content Subject to Water Alternating Gas Processes

• Grant number: 20/13146-7
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Effective date (Start): November 01, 2020
• Effective date (End): October 31, 2024
• Field of knowledge: Engineering - Mechanical Engineering
• Acordo de Cooperação: Equinor (former Statoil)
• Principal Investigator: Denis José Schiozer
• Grantee: Davi Éber Sanches de Menezes
• Host Institution: Centro de Estudos de Energia e Petróleo (CEPETRO). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Host 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

Complex carbonate reservoirs with high gas content such as those found in the Brazilian pre-salt are challenging to represent in simulation models. A key difficulty is to construct reservoir models that are consistent with the physics of enhanced oil recovery (EOR), but with simulation runtimes compatible with time-consuming computationally-expensive processes such as production optimization under uncertainty. This work aims to investigate techniques to make such problems feasible while ensuring adequate representation of reservoir phenomena, such as those typical of miscible gas. We will investigate different techniques and ultimately propose a single workflow or method that makes possible to quickly evaluate and optimize multiple realizations of water alternating gas (WAG) injection process, while retaining error control. Low fidelity and proxy models are possible alternatives to be investigated in this work for optimization and characterization purposes. This study will be conducted on the benchmark case study UNISIM-II-D (Correia et al., 2015), a carbonate reservoir with pre-salt features. The advantage of using a benchmark model, where a reference answer is known (reference model UNISIM-II-R), is the ability to collect data at different stages and to test the validity of new and existing methods. (AU)