A parallel viscoplastic multiscale reservoir geomechanics simulator

Abstract

Reservoir geomechanics has already proven to play an important role in reservoir management studies. However, the computational costs of these studies usually hinders a thorough evaluation of geomechanical effects. In this paper, we present the development of a massively parallel, multiscale reservoir geomechanics simulator currently in use on industry-grade geomechanical studies. The viscoplastic formulation allows for accurate modelling of the geomechanics effects, at the same time that results on an efficient computational model. The massively parallel distributed memory implementation of a linear system framework takes the most advantage of high performance computing (HPC) infrastructures, making use of clusters of multicore nodes. A Preconditioned Conjugate Gradient solver is implemented based on this framework. An additive, coarse-space preconditioner, based on the Multiscale Finite Element (MSFE) method, allows for an efficient, fit-for-purpose, linear system solution strategy. Because the viscoplastic formulation results on a symmetric system matrix that does not change across the simulation in time (only the right hand side does), the MSFE basis-function can be built only once, hence avoiding expensive computations. Our reservoir geomechanics simulator is capable of simulating stress and strain behaviour on large real field, geologically complex, case applications, e.g. from the Brazilian pre-salt. We present different studies, which involve the investigation of geomechanical effects, namely, subsidence, thermomechanics and cap rock integrity. In these studies, we demonstrate the scalability of the simulator in real field models with up to almost 100 million elements, running on up to more than 600 computing cores. The usage of state-of-the-art simulation approaches, combined with modern HPC strategies, enables reservoir geomechanics studies which were once hampered by computational limitations. This implementation will also allow for even more computationally intensive workflows which require many simulations, e.g. uncertainty quantification.