We present a reservoir simulation framework for coupled thermal-compositional-mechanics processes. We use finite-volume methods to discretize the mass and energy conservation equations and finite-element methods for the mechanics problem. We use the first-order backward Euler for time. We solve the resulting set of nonlinear algebraic equations using fully implicit (FI) and sequential-implicit (SI) solution schemes. The FI approach is attractive for general-purpose simulation due to its unconditional stability. However, the FI method requires the development of a complex thermo-compositional-mechanics framework for the nonlinear problems of interest, and that includes the construction of the full Jacobian matrix for the coupled multi-physics discrete system of equations. On the other hand, SI-based solution schemes allow for relatively fast development because different simulation modules can be coupled more easily. The challenge with SI schemes is that the nonlinear convergence rate depends strongly on the coupling strength across the physical mechanisms and on the details of the sequential updating strategy across the different physics modules. The flexible automatic differentiation-based framework described here allows for detailed assessment of the robustness and computational efficiency of different coupling schemes for a wide range of multi-physics subsurface problems.

Accurate representation of processes associated with energy extraction from subsurface formations often requires models which account for chemical interactions between different species in the presence of multiphase flow. In this study, we focus on modeling of acid stimulation in the near-well region. For the chemical processes which include a dissolution of rock material, an issue arises with the predictive representation of flow. Taking into account the spatial scale of discretization, some of simulation control volumes can have values of porosity close to 1, which makes an application of Darcy's law inconsistent and requires employing a true momentum equation such as the Darcy-Brinkman-Stokes (DBS) equation. The DBS equation automatically switches the description between Darcy equation in control volumes with low porosity and Stokes equation in grid blocks with high porosity. For chemical reactions, we propose a local nonlinear solution technique that allows solving the balance of solid species separately yet retaining the full coupling with rest of the equations. Finally, we study the impact of multiphase flow. The DBS approach is not well established for multiphase flow description. Therefore we employ a hybrid approach, where we assume that the single-phase DBS flow and the multiphase Darcy flow occur in separate regions. We test the accuracy and performance of both approaches on realistic models of practical interest.

We present a new framework for solving coupled multi-physics problems. The objective is to develop a platform where different coupling strategies for the simulation of complex physical processes can be employed with great flexibility in order to find an optimal - in terms of robustness and computational efficiency - strategy for a given problem. The new simulator is modular; each module represents a particular physics process, such as compositional, thermal, poromechanics, reactions, wells, and surface facilities. The platform provides seamless coupling between the physics modules without resorting to conditional branches and intermediate interface and treats terms that are coupled across multiple physics modules efficiently. The different modules can be coupled with each other in a sequential or fully coupled manner, and different solution strategies can be applied to different modules. This allows investigation of complex coupling strategies that have not been studied before. Examples of target problems include modeling compositional-thermal EOR processes in both conventional and unconventional resources with tight coupling to nonlinear poromechanics. The paper addresses the design of the framework and provides details of its implementation.