Reservoir simulation predominately uses structured grids, but they have difficulties for field-scale simulation with maximum reservoir contact (MRC) wells. In this work, the grid resolution for numerical convergence using structured grids is studied. Results showed that sufficiently fine grid is needed to obtain converged solution. In common practice, the geocellular models are simply upscaled and used in history matching. This study highlights the need for grid resolution to resolve flow dynamics in reservoir simulation and improve well inflow performance calculation. Otherwise, the near-well flow may not have converged and the fidelity of the models for performance prediction is in question. Improving the fidelity of simulation results by adequately modeling the complex multiphasic flow dynamics near complex wells in full-field simulation is of paramount importance nowadays. This work introduces a full-field unstructured gridding method which can optimally place unstructured grid cells where the resolution is needed. The method produces a consistent discretization that is efficient to compute by using a parallel unstructured reservoir simulator. For a giant Middle-East carbonate reservoir that was developed primarily using complex MRC wells, unstructured grid models were used to improve the accuracy of near-well flow and to better represent well inflow performances. The unstructured grid models are compared against the original structured grid models that show computational cost saving and require few grid cells. Simulation results demonstrate an unstructured workflow that is practical and can be used to validate near-well modeling accuracy for the existing structured grid simulation results. The method is particular attractive for situation with denselyspaced complex wells where a structured local grid refinement (LGR) method will be ineffective. An unstructured grid is well suited to honor the near-well flow geometry and to focus grid resolution where it is needed. Perpendicular bisection (PEBI) grids are orthogonal by construction. This reduces computational complexity because two-point flux approximation (TPFA) can be applied. In field-scale simulation, this results in a significant improvement to accuracy and computational cost savings.


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