Developing and managing oil reserves in the Brazilian pre-salt are complex processes. There are many technological challenges related to the water depth, the lithostatic pressure and the thick layer of salt. Additional challenges arise from the different heterogeneities of the reservoir at various scales, which govern the fluid flow in the reservoir.
Besides, an important limitation to oil production in these reservoirs is often related to the high production of associated gas rich in CO2. In this context, the use of advanced oil recovery methods (IOR), such as alternating gas and water injection (WAG-CO2), is an attractive solution, both in terms of reservoir and logistics, due to its potential to increase oil recovery and safely dispose of CO2. However, the characterization of reservoirs in these contexts is a difficult process, dominated by a high degree of uncertainty and characterized by complex models with very high simulation times.
For this reason, the use of new digital field technologies appears as a promising and cutting-edge solution for optimizing production. Examples of digital initiatives include smart wells, intelligent control of oil fields, real-time data analysis and processing using machine learning methods, hardware acceleration and information visualization techniques. This technology may incur additional investments, but it adds important operational flexibility to reservoir management. However, the use of this technology is still emerging in the oil industry, which means that further research is needed to optimize its use and maximize the value of such an investment.
Faced with these challenges, RL1 was proposed with the objective of developing methodologies to improve the decision-making and optimization processes related to the development and management of the Brazilian pre-salt oil reserves. The research developed by RL1 follows the concept of development and management of closed-loop oil fields (CLFDM), as illustrated in the image below.
Desenvolvimento e gerenciamento dos campos de petróleo em malha fechada (Schiozer et al., 2019).
Why do we do it?
The development and management of oil reserves in Brazilian pre-salt reservoirs are complex processes. The prediction of fluid flows in the reservoir is extremely challenging due to the different heterogeneities in the different scales and complex composition fluids. Uncertainties are always present during high investment decision processes.
How do we do it?
Using numerical reservoir simulations, data-based methods, machine learning, advanced optimization techniques, information visualization and hardware acceleration.
What do we do?
We developed methodologies to improve the decision-making and optimization processes related to the development and management of oil reservoirs in the Brazilian pre-salt.
Currently, RL1 develops researches in five main areas, called Activities (AT):
- AT1: Reservoir characterization and representation in simulation models;
- AT2: Production optimization and decision-making under uncertainty;
- AT3: Simulation of oil recovery methods;
- AT4: Simulation and visualization of models;
- AT5: Integration of reservoirs with production systems;