Application of neural network to speed-up equilibrium calculations in compositional reservoir simulation

Abstract

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results, where hundred or even thousand of different runs are performed. In this work, a new methodology using artificial intelligence to learn the thermodynamic equilibrium is proposed. This algorithm is used to replace the classical equilibrium workflow in reservoir simulation. The new method avoids the stability test for single-phase cells in most cases and provides an accurate two-phase flash initial estimate. The classical and the new workflow are compared for a gas-oil mixing case, showing a simulation time speed-up of approximately 50%. The new method can be used in compositional reservoir simulations.