High performance framework for modelling of complex subsurface flow and transport applications

Numerical modelling of multiphase multicomponent flow coupled with mass and energy transport in porous media is crucially important for many applications including oil recovery, carbon storage and geothermal. To deliver robust simulation results, a fully or adaptive implicit method is usually employed, creating a highly nonlinear system of equations. It is then solved with the Newton-Raphson method, which requires a linearization procedure to assemble a Jacobian matrix. Operator Based Linearization (OBL) approach allows detaching property computations from the linearization stage by using piece-wise multilinear approximations of state-dependent operators related to complex physics. The values of operators used for interpolation are computed adaptively in the parameter-space domain, which is uniformly discretized with the desired accuracy. As the result, the simulation performance does not depend on the cost of property computations, making it possible to use expensive equation-of-state formulations (e.g., fugacity-activity thermodynamic models) or even black-box chemical packages (e.g., PHREEQC) for an accurate representation of governing physics without penalizing runtime. On the other hand, the implementation of the simulation framework is significantly simplified, which allows improving the simulation performance further by executing the complete simulation loop on GPU architecture. The integrated open-source framework Delft Advanced Research Terra Simulator (DARTS) is built around the OBL concept and provides a flexible, modular and computationally efficient modelling package. In this work, we evaluate the computational performance of DARTS for various subsurface applications of practical interests on both CPU and GPU platforms. We provide a detailed performance comparison of particular workflow pieces composing Jacobian assembly and linear system solution, including both stages of Constrained Pressure Residual solver.