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This work studies the performance of a novel preconditioner, designed forthermal reservoir simulation cases and recently introduced in Roy et al. (2020)and Cremon et al. (2020), on large-scale thermal CO2 injection cases. ForCarbon Capture and Sequestration (CCS) projects, injecting CO2 undersupercritical conditions is typically tens of degrees colder than the reservoirtemperature. Thermal effects can have a significant impact on the simulationresults, but they also add many challenges for the solvers. More specifically,the usual combination of an iterative linear solver (such as GMRES) and theConstrained Pressure Residual (CPR) physics-based block-preconditioner is knownto perform rather poorly or fail to converge when thermal effects play asignificant role. The Constrained Pressure-Temperature Residual (CPTR)preconditioner retains the 2x2 block structure (elliptic/hyperbolic) of CPR butincludes the temperature in the elliptic subsystem. The elliptic subsystem isnow formed by two equations, and is dealt with by the system-solver ofBoomerAMG (from the HYPRE library). Then a global smoother, ILU(0), is appliedto the full system to handle the local, hyperbolic temperature fronts. Weimplemented CPTR in the multi-physics solver GEOS and present results onvarious large-scale thermal CCS simulation cases, including both Cartesian andfully unstructured meshes, up to tens of millions of degrees of freedom. TheCPTR preconditioner severely reduces the number of GMRES iterations and theruntime, with cases timing out in 24h with CPR now requiring a few hours withCPTR. We present strong scaling results using hundreds of CPU cores formultiple cases, and show close to linear scaling. CPTR is also virtuallyinsensitive to the thermal Peclet number (which compares advection anddiffusion effects) and is suitable to any thermal regime.

Constrained Pressure-Temperature Residual (CPTR) Preconditioner Performance for Large-Scale Thermal CO2 Injection Simulation