Detailed Surfactant Model Construction Elucidates Benefits of Cross-Flow in Fluvial Heterogenous Surfactant-Polymer Pilot in Grimbeek

Summary After an 18%STOOIP incremental oil polymer pilot we have developed the surfactant-polymer(SP) formulation to recover the residual oil. The SP formulation has a viscosity more than 1.5 times greater than oil viscosity. The Grimbeek reservoir is a heterogeneous multilayer fluvial system with many surfaces of contact between high permeability and low permeability. Increasing oil recovery because of induced flow from low permeability to high permeability driven by a high viscosity slug has been around for more than 30 years. This phenomenon occurs when there is higher pressure drop across a viscous slug. Does the cross-flow mechanism (Sorbie2019) that increased the polymer flow recovery benefit the surfactant-polymer flooding? How is this mechanism affected by factors such, removal of residual oil, surfactant concentration, slug size, salinity changed, retention and injection strategy? To answer these questions, we construct a detailed surfactant model in a compositional simulator that captures the multiscale nature of the multiple surfaces of contact created by the fluvial depositional environment. This realistic representation of the subsurface poses challenges to the numerical methods in the compositional simulator. Through modelling the fluvial geometry in a compositional simulator, our simulation reveals that the viscosity overdesigned of the surfactant-polymer formulation favours accessing to more residual oil. Starting from a black oil model, the work was divided into four main tasks. First, converting the BlackOil PVT Model to a compositional model, followed by creating trajectories and perforations in an unstructured grid that brings complexities to the typical well-tracking task to place wells in corner point grids. Third, the compilation of the historical production of oil and gas as well as the water injected and polymer. We automate the input deck using visual basic and Python scripts that now are useful for any source file. Based on them, we can propose the most suitable injection strategy. This result indicates that when the geological setting is heterogeneous is better to increase formulation viscosity (it depends on the formulation, but this usually means to increase surfactant concentration) and avoid the typical EOR workflow of formulation optimization to reduces surfactant concentration. Our simulation elucidates the efficacy of increasing the formulation concentration to reduce the slug size. And It improves our understanding of the interplay between viscosity and capillary forces. Also, we developed different scripts that allow us to easily obtain the dataset for our compositional simulator.