Challenges on EOR process upscaling have been discussed extensively in the industry and effects of diffusion, dispersion, heterogeneity, force balance and frontal velocity -among others, recognized and qualified, along with the importance of understanding the numerical model finite difference equations and modeling strategy. Augmenting the upscaling complexity is the often-limited understanding/data on the EOR displacement at different scales (from micro to full field), including the EOR agent/rock/fluid interactions that is often available at the early stages of the EOR process de-risking. A common denominator for the EOR process characterization and upscaling (along with the discretization of the displacement) is the non-uniqueness nature of the problem. As the complexity of numerical representation of the EOR process increases (thus increasing data characterization requirements), so does the number of plausible solutions and challenges when dealing with an otherwise incomplete dataset. Digital rock has evolved as a strong alternative to complement laboratory corefloods, allowing for EOR agent optimization on a high-resolution digital representation of the pore structure, detailed digital fluid model of both reservoir fluids and EOR agents and physical rock-EOR agent-reservoir fluid interaction, thus providing several calibration points to ensure the finite-difference model calibration and upscaling preserve the process behavior. This paper discusses the use of digital rock solutions on the EOR deployment, particularly on translating the results to numerical finite difference models, addressing the inherent laboratory measurement uncertainty and proposing a fit-for-purpose multi-scale upscaling strategy that addresses both effects of heterogeneity and EOR agent characterization during the upscale process. This paper addresses the challenges of chemical flooding upscaling, particularly polymer by using a real-life polymer injection case where digital rock, corefloods and more importantly pilot results are available to test and validate our observations. Using a polymer coreflood and digital rock results as input, numerical finite difference simulation models were built and calibrated to effectively reproduce the displacement physics observed on both digital rock and corefloods, digital flood results were used to bridge the laboratory-to-numerical model step by providing effective upscaled polymer properties as well as intrinsic rock properties such as relative permeability and capillary pressures, which are then taken through a series of multi-scale finite difference models to identify, validate and quantify upscaling requirements, addressing polymer deformation through pore throats and effective simulation viscosity. Digital rock is used to rank and resolve ambiguity on the finite difference model calibration by providing an otherwise rare opportunity to visualize the displacement in the 3D space. The analysis shed a new light on fluid-fluid and fluid-rock interaction at pore scale and enabled us to improve on the finite difference model generation and polymer properties.

Use of numerical models to characterize and evaluate reservoir potential is an industry wide practice, with increasingly more development decisions being substantiated by finite difference models. Advances on hardware and software, along with the ability to effectively incorporate accurate process physics, makes simulation a robust tool for field development decisions, particularly on complex operations such as enhanced oil recovery and/or reservoirs with challenging heterogeneity and pore structures. Use of these models does not come without its challenges where data requirements (and use of special characterization both at lab and field level) increase as does the reservoir characterization granularity and thus model sizes. Unsurprisingly the increase of model precision and data requirements amplifies non-uniqueness of the numerical solutions obtained during any field evaluation including field development planning (FDP). Incomplete/inconsistent datasets pose a further challenge to the accuracy (and arguably risk) of the forecasts by introducing further uncertainty on the process characterization. Use of complementary technology such as digital rock, that would enable mitigate impact of such uncertainties in a timely manner -either at field or laboratory level, is thus highly desirable particularly when dealing with enhanced oil recovery. Compounding the non-linearity effect of the EOR agent characterization is the effect of the augmented numerical artifacts (dispersion, dilution, etc) of which complex chemical implementations are prone to, making the upscaling process from laboratory dimensions to field more complex. This paper complements our previous investigation on the use of digital rock solutions and multi-scale upscaling and is addressing two complementing topics: Use of multiscale digital rock technology for field development – using a case study to illustrate the use of DR on field appraisal complementing otherwise unsampled facies, using a multi-nested approach to reconcile DR observations at different plug scalesEvaluate the impact of finite-difference numerical simulation grid on the surfactant injection performance- highlighting limitations and challenges of existing models as well as proposing potential upscaling alternatives. It is our intention to further reconcile digital rock upscaling with other EOR methods such as polymer/CO2 injection and of course surfactant. While we were able to highlight the caveats of upscaling on complex chemical floods we continue to investigate and design a solution that would encompass combination of chemicals (surfactant, alkaline and polymer) as well as handle of concentration/salinity changes.