Phase behavior computations using Gibbs free energy minimization on GPUs for speeding up compositional simulations

CO2 flooding in a preferred method of enhancing oil recovery as it has the dual benefit of sequestration and increasing oil recovery. In order to evaluate and design these processes, compositional simulations are used to track component changes during the course of flow. However, the phase behavior, as well as the stability computations associated with compositional simulations are time-consuming. In a compositional reservoir simulator, EOS, typically a cubic EOS (i.e., Peng-Robinson) for hydrocarbons, must be solved in all the grid blocks at every time step in order to accurately predict the phase behavior. In typical field-scale simulations, millions of grid blocks are used, and hence solving EOS for multicomponent hydrocarbon systems really slows down the simulation. There is a need to increase the speed and improve the efficiency of phase equilibrium computation for field-scale simulations. In this research, we develop a Gibbs free energy framework optimized for graphic processing unit (GPU) implementation. The Gibbs free energy minimization is preferred as it is a unifying function to combine components described using different thermodynamic models - Equation of State (EOS) as well as activity coefficient models. We use a combination of CPUs and GPUs to solve a constrained minimization problem and the solution is the equilibrium composition at a fixed temperature and pressure (PT flash). The NVIDIA Tesla GPUs help parallelize multiple functions evaluations simultaneously, which results in a significant speedup in computation times. A comparison of computation times of our approach with other approaches to compute and also algorithms to obtain equilibrium compositions is presented -1. CPU versus GPU. 2. K-value versus Gibbs free energy approach. The proposed model can easily be incorporated into existing reservoir simulators to decrease computational times.