Research Lines

 

 

 

Enhanced Oil Recovery / Improved Oil Recovery: advanced recovery methods require replacing the black-oil simulation with the compositional and thermal simulation required for multiphase flow as well as the heat and mass transfer of oil and gas phase components. Understanding and modeling the physical processes of these simulations becomes a crucial step in this research. This line aims to optimize and evaluate different methods such as miscible, thermal and chemical, in order to increase oil recovery, considering the additional costs involved in each method;

 

 

Reservoir Management: all advanced recovery methods incur additional costs. Field management requires appropriate economic evaluation, considering the additional costs and revenues of the recovery processes implemented;

 

 

Exploitation Optimization: optimizing advanced recovery processes requires efficient and robust optimization methods. The most promising candidates today are genetic algorithms (GA) and ensemble optimization (EnOpt). These methods are free of gradient calculation, requiring less computational time. This line seeks to apply and develop them in the most different applications;

 

 

Methods to Decrease Simulation Time: as current simulations demand a higher computational cost, it is necessary to study techniques to accelerate the process. There are basically two ways to accomplish this step. One is by substitution and the other internally in the numerical calculations of the simulator. These include surrogate models and order reduction models, respectively. Substitute models include: response surfaces, kriging, experimental design, neural networks, among others. Among the best known order reduction models are proper orthogonal decomposition (POD), trajectory piecewise linear (TPWL), hybrid method POD-TPWL, etc. This line seeks to integrate these methods and improve them to achieve the reduction of simulation time, without compromising the quality of the results;

 

 

Caracterization of Carbonate Reservoirs: experimental analysis of rocks, fluids, and understanding rock-fluid and fluid-fluid interactions of samples becomes vital for understanding the behavior and modeling of pre-salt carbonate reservoirs for simulation studies. In the CO2-WAG system, for example, it is also necessary to evaluate the wettability of rocks due to the hysteresis effect, dissolution effects, solubility, etc. This line seeks the best characterization of carbonate reservoirs, as well as the multiscale integration of the various techniques;

 

 

Intelligent Well Completion (IWC): as the pre-salt reservoirs are very heterogeneous, the application of intelligent completion is recommended. This line aims to study the best application conditions and ways to control the valves, always evaluating the risk versus return;

 

 

Optimization under Uncertainties: in the development of an oilfield there are three main uncertainties, economic, geological and technical uncertainty. Taking these probabilistic scenarios into consideration makes the process closer to the reality of an E&P project, generating more reliable estimates;

 

 

History Matching: studies in the literature have successfully employed ensemble smoother methods in history matchin of reservoir models. This line aims the application and development of these techniques in the improvement of the macthing;

 

Hydraulic Fracking / Acidification: it aims to evaluate the increase of recovery in reservoirs subjected to hydraulic fracturing or acidification, taking into account the costs to perform these operations. Currently this technique has been widely used in shale / tight gas reservoirs in the USA, seeking to release the gas trapped in the pores of these low permeability reservoirs. In Brazil, acidification fracturing has been employed in the pre-salt carbonate reservoirs;

 

 

Real-Time Optimization: real-time optimization with the reservoir in production requires the acquisition of production data, history matching and optimization of production parameters with the adjusted model. This process becomes cyclical, since the simulation model should honor production data throughout the life of the field;

 

 

Real Options Theory: it seeks to capture the value of the flexibility of E&P projects due to the various sources of uncertainty embedded, whether economic, geological or technical / operational. This line seeks to make the application of TOR in the oil industry increasingly practical.