Simplifications to make a CLFDM practice considering the time available

• Grant number: 24/10547-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: September 01, 2024
• End date: August 31, 2028
• Field of knowledge: Engineering - Mechanical Engineering
• Agreement: Equinor (former Statoil)
• Principal Investigator: Denis José Schiozer
• Grantee: Marx Vladimir de Sousa Miranda
• 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

Although continuous advances in the computational domain were observed in the past decades, numerical simulation is still challenging when applied for some kind of fields, due some characteristics: level of heterogeneity, field size, necessity to use compositional simulation, among others. When performing probabilistic procedures that demand high number of simulation runs, the time spent for model-based decision analysis can become very high, making the application of these procedures unfeasible in practical applications. In order to minimize the risks involved in the project, Schiozer et al. (2019) proposed a 12-steps model-based methodology for dealing with uncertainties to assist the decision-making during the development and production phase of oil fields (closed-loop field development and management - CLFDM). The methodology involves reservoir simulations, risk analysis, data assimilation, uncertainty reduction techniques, selection of representative models and selection of production strategies under uncertainty, in 12 steps that can be repeated as new information is acquired and inserted to the studies. Although a promising process, it can be very timeconsuming for practical applications, depending on the complexity of the simulation models and the number of simulation runs. Thus, the objective of this project is to evaluate possible simplifications to be made in the process or in the simulation models to speed-up the decision analysis process, without losing accuracy in the results and without affecting the final decisions. (AU)