Application of Digital Rock Technology for Chemical EOR Screening

Abstract

Fast and reliable EOR process selection is a critical step in any EOR project. The digital rock (DR) approach jointly developed by Shell and SLB is aimed to be the smallest scale yet advanced EOR Pilot technology. In this document, we describe the application of DR technology for screening of different EOR mechanisms at pore-scale focused to enhance recovery from a particular reservoir formation. For EOR applications DR brings unique capabilities as it can fully describe different multiphase flow properties at different regimes. The vital part of the proposed approach is the high-efficient pore-scale simulation technology called Direct Hydrodynamics (DHD) Simulator. DHD is based on a density functional approach applied for hydrodynamics of complex systems. Currently, DHD is benchmarked against multiple analytical solutions and experimental tests and optimized for high performance (HPC) computing. It can handle many physical phenomena: multiphase compositional flows with phase transitions, different types of fluid-rock and fluid-fluid interactions with different types of fluid rheology. As an input data DHD uses 3D pore texture and composition of rocks with distributed micro-scale wetting properties and pore fluid model (PVT, rheology, diffusion coefficients, and adsorption model). In a particular case, the pore geometry comes from 3D X-ray microtomographic images of a rock sample. The fluid model is created from lab data on fluid characterization. The output contains the distribution of components, velocity and pressure fields at different stages of displacement process. Several case studies are demonstrated in this work and include comparative analysis of effectiveness of applications of different chemical EOR agents performed on digitized core samples.

Nanoscale imaging of pore‐scale fluid‐fluid‐solid contacts in sandstone

“…Digital rock models [Andrä et al, 2013] have allowed for about a decade, computation of these average parameters from pore-scale models. Pore network models in which both pore space and flow are simplified to a substantial degree, and more direct pore scale approaches using lattice Boltzmann [Coles et al, 1998;Ramstad et al, 2009], direct Navier-Stokes [Raeini et al, 2014], and other methods [Armstrong et al, 2013] have, for coarse grained rocks, produced results which compare well with bulk measurements [Andrä et al, 2013]. Here fluid flow is modeled in the pore space, which is quantified by, e.g., micro X-ray computed tomography (micro-CT) [Coles et al, 1998].…”

Shear Rate Determination from Pore-Scale Flow Fields

“…In order to exclude potential polymer-specific effects such as permeability reduction by polymer adsorption, and more complex rheology on the pore scale such as extensional viscosity (Koroteev et al 2013), in the following only Newtonian fluids are considered, i.e., the power law index n = 1. The situation is further simplified to single-phase flow, i.e., S w = 1, k r,w = 1.…”

“…Depending on the polymer formulation, these effects sometimes play a significant role in controlling the mobility reduction factor (Sorbie 1991;Rodriguez et al 2014). The increase in in situ viscosity is a combination of rheological effects such as shear-dependent fluid viscosity, extensional viscosity (Koroteev et al 2013;van der Plas and Golombok 2015) and visco-elasticity (Wang et al 2010). Polymer in general and more specifically aqueous solutions of hydrosoluble polymer including HPAM exhibit a shear rate-dependent bulk viscosity (Delshad et al 2008).…”

Digital Rock Technology for Quantitative Prediction of Acid Stimulation Efficiency in Carbonates

“…The DHD simulator is able to capture shear-thinning and shear-thickening fluid rheology as well as more general Herschel-Bulkley rheology of the phase. Several examples of DHD simulations for specific problems in oil and gas industry are provided in the literature [19].…”