Dynamic Local Grid Refinement for Multiple Contact Miscible Gas Injection
Authors: Suicmez Vural Sander , van Batenburg Diederik W. , Matsuura Tsuyoshi , Bosch Marco , Boersma Diederik Michiel 
Published: May 2016

Abstract
Accurate modeling of enhanced oil recovery (EOR) processes requires higher resolution, both in space and in time, than typical black-oil simulations. In addition, most EOR techniques result in the simultaneous flow of multiple components and phases which are often modeled by employing compositional simulations. In a compositional simulation the large number of components as well as the computational costs associated with the flash calculations makes it impractical to perform a finegrid simulation.
Multiple contact miscible (MCM) gas injection is an example of an EOR process that requires an accurate simulation of multi-component and multi-phase fluid flow with an appropriate resolution. Results of compositional simulations are sensitive to the grid block size. Obtaining an accurate result may require a significant number of small grid-blocks. However, the simulation of such processes on a high resolution grid throughout a realistic reservoir model is still not practical with today's computing power. One solution is to start on a coarse grid that is dynamically adjusted to provide sufficient resolution where and when needed.
We test and validate the applicability of dynamic local grid refinement (DLGR) for multiple contact miscible (MCM) gas injection simulations and identify appropriate criteria for the implementation of DLGR for MCM gas injection simulations.
We first conduct 1-D slim-tube simulations with varying number of grid blocks to analyze the impact of numerical dispersion on MCM gas injection processes. We then construct a 2-D reservoir model and perform a grid sensitivity analysis by refining the grid blocks both in the horizontal and the vertical directions. We then extend our model into 3 dimensions and introduce vertical heterogeneities. We conduct simulations by implementing dynamic local grid refinement in each direction. We show that the results are in good agreement with fine-scale simulations, while the number of active grid blocks and computational time are significantly reduced. We then perform a sensitivity analysis in order to illustrate that the efficiency of the dynamic grid refinement technique depends on different parameters such as the heterogeneity of the reservoir, gas injection rate and the number of components in the system.

Introduction
Accurate and efficient modeling techniques are essential for the development of and the recovery of oil from hydrocarbon reservoirs. Reservoir performance forecasts are conventionally performed via finite-difference simulators. An important shortcoming of these simulations is the numerical dispersion which emerges from discretization errors introduced by the finite-difference representation of the convective term [1].
Extra care should be taken while modeling an enhanced oil recovery (EOR) process as this usually requires an accurate representation of the specific interactions not only between the phases but also between the fluids and the reservoir rock. Miscible gas injection is example of an EOR process where multiple components and phases flow simultaneously through the pore space of the rock. Depending on the composition of the injected fluid and the fluid originally residing in the reservoir, components may prefer to move from one phase to another at specific temperature and pressure conditions. This behavior is addressed via flash calculations using a compositional simulation that uses one of several available equations of state (EOS) models.