Oil reservoirs have structural heterogeneities across multiple length scales and, particularly in carbonates, complexly distributed wettabilities. The interplay of structural and wettability heterogeneities is the fundamental control for sweep efficiency and oil recovery. This interplay must be captured in physically robust flow functions, such as relative permeability and capillary pressure functions. Such flow functions then allow us to choose the best improved-oil-recovery (IOR) or enhanced-oil-recovery (EOR) process and forecast oil recovery with adequate precision. Obtaining flow functions for reservoir rocks with varying wettability is a challenging task, especially when three fluid phases coexist. In this work, we use pore-network modeling, a reliable and physically based simulation tool, to predict three-phase flow functions. We have developed a new pore-scale network model for rocks with variable wettability. Unlike other models, this model combines three new and important features. (1) Our network model comprises a novel thermodynamic criterion for the formation and collapse of oil layers. This captures film/layer flow of oil adequately, which affects the oil relative permeability at low oil saturation. We can therefore predict residual oil more accurately.(2) We implemented multiple displacement chains, in which injection of one phase at the inlet triggers a chain of interface displacements throughout the network. This allows us to accurately model the mobilization of disconnected phase clusters that arise during higher-order [water-alternating-gas (WAG)] floods. Again, this feature is key to a better prediction of residual oil saturation (ROS).(3) Our model takes realistic 3D pore networks extracted from pore-space reconstruction methods and X-ray computerizedtomography (CT) images as input. This preserves both topology and pore shape of the rock, providing better estimates of phase conductivities and relative permeability. We have validated our model by use of available experimental data for a range of wettabilities and demonstrated the impact of single vs. multiple displacement on residual oil. We also used a proof-of-concept study in which we use flow functions for different wettabilities that have been computed with our model in field-scale reservoir simulations to forecast oil recovery during tertiary gas injection. These results are compared with predictions that used empirical flow functions. Flow functions computed by our network model gave higher oil recovery than corresponding flow functions calculated by empirical models; oil recovery increases with decreasing water-wetness. This shows that the pore-scale physics encapsulated in our new network model leads to the right emergent behavior at the reservoir scale.

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Carbonate reservoirs have structural heterogeneities (triple porosity: pore-vug-fracture) and are mixed-to oil-wet. The interplay of structural and wettability heterogeneities impacts the sweep efficiency and oil recovery. The choice of an IOR or EOR process and the prediction of oil recovery requires a sound understanding of the fundamental controls on fluid flow in mixed-to oil-wet carbonate rocks and physically robust flow functions, i.e. relative permeability and capillary pressure functions. Obtaining these flow functions is a challenging task, especially when three fluid phases coexist. In this work we use pore-network modelling, a reliable and physically-based simulation tool, to predict three-phase flow functions. We have developed a new pore-scale network model for rocks with variable wettability. Unlike other models, this model comprises a novel thermodynamic criterion for formation and collapse of oil layers. The new model hence captures film/layer flow of oil adequately which impacts the oil relative permeability at low oil saturation and hence the accurate prediction of residual oil. Pore-networks extracted from pore-space reconstruction methods and CT images have been used as input for our simulations and the model comprises a constrained set of parameters that can be tuned to mimic the wetting state of a given reservoir. We have validated our model with available experimental data for a range of wettabilities. A sensitivity analysis has been carried out to investigate the dependency of relative permeabilities on layer collapse and film/layer flow under various wetting conditions. Additionally, WAG injection has been simulated with different lengths of so-called multi-displacement chains and different flood end-points. The flow functions generated by our model can be passed to the next scales (upscaling) to predict the oil recovery at the reservoir scale and we demonstrate this using a proof-of-concept study.

Naturally Fractured Reservoirs (NFR) contain a significant amount of remaining petroleum reserves and are now being considered for water-alternating-gas (WAG) flooding as secondary or tertiary recovery. Reservoir simulation of WAG is very challenging even in non-fractured reservoirs because a proper set of saturation functions that describe the underlying physics is vitally important but associated with high uncertainty. For NFRs, another challenge is the upscaling of recovery processes, particularly the fracture-matrix transfer during three-phase flow, to the reservoir scale using dual-porosity or dual-permeability models. In this work, we approach a solution to this challenge by building models at various scales, starting from pore-scale to an intermediate scale then to the reservoir scale. We show how pore-network modelling and fine grid modelling where the fractures and matrix are represented explicitly can be used to increase the accuracy of numerical simulations at the field-scale in order to predict recoveries for NFR during WAG. We study the sensitivity to WAG design parameters as well as the impact of matrix wettability on recovery. We also compare the fine grid model with an equivalent dual-porosity model. Simulation at an intermediate scale showed at least 10% absolute change in recovery due to the choice of the empirical three-phase relative permeability model. In fine grid simulation with physically consistent pore-network derived three-phase relative permeability and capillary pressure, injected water and gas are predicted to displace each other, leaving oil behind, therefore reducing WAG efficiency. For this case, empirical models over-estimate recovery by 25%. Classical dual-porosity model over-estimates recovery during the early WAG cycles, and fails to adequately match recovery of the fine grid simulation. Our multi-scale simulation approach identifies important factors and uncertainties when considering WAG flooding in NFR. It provides a methodology through which WAG recovery can be estimated using available technology while preserving the pore-scale physics for three-phase flow, which are crucial to making reliable forecasts at the reservoir scale.