Abstract
We demonstrate how to use numerical simulation models directly on micro-CT images to understand the impact of several enhanced oil recovery (EOR) methods on microscopic displacement efficiency. To describe the physics with high-fidelity, we calibrate the model to match a water-flooding experiment conducted on the same rock sample (Akai et al. in Transp Porous Media 127(2):393–414, 2019. https://doi.org/10.1007/s11242-018-1198-8). First we show comparisons of water-flooding processes between the experiment and simulation, focusing on the characteristics of remaining oil after water-flooding in a mixed-wet state. In both the experiment and simulation, oil is mainly present as thin oil layers confined to pore walls. Then, taking this calibrated simulation model as a base case, we examine the application of three EOR processes: low salinity water-flooding, surfactant flooding and polymer flooding. In low salinity water-flooding, the increase in oil recovery was caused by displacement of oil from the centers of pores without leaving oil layers behind. Surfactant flooding gave the best improvement in the recovery factor of 16% by reducing the amount of oil trapped by capillary forces. Polymer flooding indicated improvement in microscopic sweep efficiency at a higher capillary number, while it did not show an improvement at a low capillary number. Overall, this work quantifies the impact of different EOR processes on local displacement efficiency and establishes a workflow based on combining experiment and modeling to design optimal recovery processes.
Highlights
Pore-scale imaging and modeling methods which use high-resolution images of pore space to compute physical properties of rocks are a valuable tool to understand the physics of processes occurring in the pore space
We demonstrate how to use numerical simulation models directly on micro-CT images to understand the impact of several enhanced oil recovery (EOR) methods on microscopic displacement efficiency
In pore network modeling (PNM), a pore network that represents the 3D pore structure is first extracted, multiphase flow in the network is solved based on quasi-static displacement governed by local capillary pressure (Blunt 2001), while in direct numerical simulation (DNS) the Navier–Stokes equation for multiphase flow is directly solved through the 3D pore structure
Summary
Pore-scale imaging and modeling methods which use high-resolution images of pore space to compute physical properties of rocks are a valuable tool to understand the physics of processes occurring in the pore space. These methods have been used to study multiphase flow in porous media, which plays an important role in a wide range of engineering applications such as hydrocarbon recovery (Zubair 2012), CO2 sequestration in underground saline reservoirs (Kimbrel et al 2015) and remediation of oil contaminated soil (Porter et al 2010). DNS is computationally more expensive than PNM, it avoids uncertainty in pore network extraction and more rigorously models wetting phenomena in pore spaces with complicated geometries
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