Effective Modeling Approaches for CO2 EOR Developments
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We present a simulation study of a mature reservoir for COv2 Enhanced Oil Recovery (EOR) development. This project is currently recognized as the world’s largest project utilizing post-combustion COv2 from power generation flue gases. With a fluvial formation geology and sharp hydraulic conductivity contrasts, this is a challenging and novel application of COv2 EOR. The objective of this study is to obtain a reliable predictive reservoir model by integrating multi-decadal production data at different temporal resolutions into the available geologic model. This will be useful for understanding flow units, heterogeneity features and their impact on subsurface flow mechanisms to guide the optimization of the injection scheme and maximize COv2 sweep and oil recovery from the reservoir. Our strategy consists of a hierarchical approach for geologic model calibration incorporating available pressure and multiphase production data. The model calibration is carried out using regional multipliers whereby the regions are defined using a novel Adjacency Based Transform (ABT) accounting for the underlying geologic heterogeneity. The Genetic Algorithm (GA) is used to match 70-year pressure and cumulative production by adjusting pore volume and aquifer strength. This leads to an efficient and robust workflow for field scale history matching. The history matched model provided important information about reservoir volumes, flow zones and aquifer support that led to additional insight to the prior geological and simulation studies. The history matched field-scale model is used to define and initialize a detailed fine-scale model for a COv2 pilot area which will be utilized for studying the impact of fine-scale heterogeneity on COv2 sweep and oil recovery. The uniqueness of this work is the application of a novel geologic model parameterization and history matching workflow for modeling of a mature oil field with decades of production history and which is currently being developed with COv2 EOR. In addition to the history matching studies, we developed an embedded discrete fracture model (EDFM) which is currently recognized as a promising alternative to conventional fracture modeling approaches including multiple continuum models and unstructured discrete fracture models because of its accuracy and computational efficiency. We tested the developed model with several examples including water flood and COv2 flood scenarios and confirmed applicability of the EDFM.
Onishi, Tsubasa (2017). Effective Modeling Approaches for CO2 EOR Developments. Master's thesis, Texas A & M University. Available electronically from