Improving Chemical EOR Simulations and Reducing the Subsurface Uncertainty Using Downscaling Conditioned to Tracer Data

Victor A. Torrealba, Hussein Hoteit, Adwait Chawathe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Recovery mechanisms are more likely to be influenced by grid-block size and reservoir heterogeneity in Chemical EOR (CEOR) than in conventional Water Flood (WF) simulations. Grid upscaling based on single-phase flow is a common practice in WF simulation models, where simulation grids are coarsened to perform history matching and sensitivity analyses within affordable computational times. This coarse grid resolution (typically about 100 ft.) could be sufficient in WF, however, it usually fails to capture key physical mechanisms in CEOR. In addition to increased numerical dispersion in coarse models, these models tend to artificially increase the level of mixing between the fluids and may not have enough resolution to capture different length scales of geological features to which EOR processes can be highly sensitive. As a result of which, coarse models usually overestimate the sweep efficiency, and underestimate the displacement efficiency. Grid refinement (simple downscaling) can resolve artificial mixing but appropriately re-creating the fine-scale heterogeneity, without degrading the history-match conducted on the coarse-scale, remains a challenge. Because of the difference in recovery mechanisms involved in CEOR, such as miscibility and thermodynamic phase split, the impact of grid downscaling on CEOR simulations is not well understood. In this work, we introduce a geostatistical downscaling method conditioned to tracer data to refine a coarse history-matched WF model. This downscaling process is necessary for CEOR simulations when the original (fine) earth model is not available or when major disconnects occur between the original earth model and the history-matched coarse WF model. The proposed downscaling method is a process of refining the coarse grid, and populating the relevant properties in the newly created finer grid cells. The method considers the values of rock properties in the coarse grid as hard data, and the corresponding variograms and property distributions as soft data. The method honors the fluid material balance and geological features from the coarse model. A workflow is outlined to address uncertainties in geological properties that can be reduced by integrating dynamic data such as sweep efficiency from interwell tracers. We provide several test cases and demonstrate the applicability of the proposed method to improve the history-match of a chemical EOR pilot. Further, we evaluate the fitness of different heterogeneity measures for grid-ranking of CEOR processes.
Original languageEnglish (US)
Title of host publicationSPE Annual Technical Conference and Exhibition
PublisherSociety of Petroleum Engineers (SPE)
StatePublished - Oct 9 2017


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