Abstract
In this work, we show how the a posteriori error estimation techniques proposed in [Di Pietro et al. (2014) Computers & Mathematics with Applications 68 , 2331-2347] can be efficiently employed to improve the performance of a compositional reservoir simulator dedicated to Enhanced Oil Recovery (EOR) processes. This a posteriori error estimate allows to propose an adaptive mesh refinement algorithm leading to significant gain in terms of the number of cells in mesh compared to a fine mesh resolution, and to formulate criteria for stopping the iterative algebraic solver and the iterative linearization solver without any loss of precision. The emphasis of this paper is on the computational cost of the error estimators. We introduce an efficient computation using a practical simplified formula that can be easily implemented in a reservoir simulation code. Numerical results for a real-life reservoir engineering example in three dimensions show that we obtain a significant gain in CPU times without affecting the accuracy of the oil production forecast.
Highlights
Reservoir simulation models are nowadays a key element for oil and gas companies in the development of fields
To improve the runtime performance of our simulator, we elaborated two strategies: the first consists in improving our linear solver by reducing the number of iterations using adaptative linear stopping criteria based on the a posteriori error estimate framework; the second strategy consists in reducing the global number of degrees of freedom by reducing the number of grid blocks of the mesh combining the Arcane adaptive mesh refinement feature to an advanced local space a posteriori error estimator
With our new a posteriori error estimation framework, we have improved the performance of our compositional reservoir simulator dedicated to Enhanced Oil Recovery (EOR) processes
Summary
Reservoir simulation models are nowadays a key element for oil and gas companies in the development of fields. Abstract frameworks for a posteriori estimates appeared, for two-phase flow [8, 9], multiphase compositional model [10], and the thermal multiphase compositional global model [11] In these frameworks the dual norm of the residual augmented by a nonconformity evaluation term is controlled by fully computable estimators decomposed into space, time, linearization, and algebraic error components. In this paper we propose an engineering approach of the a posteriori error estimates, provided by recent works [10, 11], in order to establish an efficient computation of the estimators in such a way that we can ensure important computational savings for realistic complex models.
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