Olalotiti-Lawal, Feyi (Texas A&M University) | Onishi, Tsubasa (Texas A&M University ) | Kim, Hyunmin (Texas A&M University ) | Datta-Gupta, Akhil (Texas A&M University ) | Fujita, Yusuke (JX Nippon Oil & Gas Exploration Corporation) | Hagiwara, Kenji (JX Nippon Oil & Gas Exploration Corporation)
We present a simulation study of a mature reservoir for carbon dioxide (CO2) enhanced-oil-recovery (EOR) development. This project is currently recognized as the world’s largest project using post-combustion CO2 from power-generation flue gases. With a fluvial formation geology and sharp hydraulic-conductivity contrasts, this is a challenging and novel application of CO2 EOR. The objective of this study is to obtain a reliable predictive reservoir model by integrating multidecadal production data at different temporal resolutions into the available geologic model. This will be useful for understanding flow units along with heterogeneity features and their effect on subsurface flow mechanisms, to guide the optimization of the injection scheme and maximize CO2 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 performed using regional multipliers, and the regions are defined using a novel adjacency-based transform accounting for the underlying geologic heterogeneity. The genetic algorithm (GA) is first used to match 70-year pressure and cumulative production by adjusting pore volume (PV) and aquifer strength. Water-injection data for reservoir pressurization before CO2 injection is then integrated into the model to calibrate the formation permeability. The fine-scale permeability distribution consisting of more than 7 million cells is reparameterized using a set of linear-basis functions defined by a spectral decomposition of the grid-connectivity matrix (Laplacian grid). The parameterization represents the permeability distribution using a few basis-function coefficients that are then updated during history matching. This leads to an efficient and robust work flow 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 compared with previous geological and simulation studies. The history-matched field-scale model is used to define and initialize a detailed fine-scale model for a CO2 pilot area that will be used to study the effect of fine-scale heterogeneity on CO2 sweep and oil recovery. The uniqueness of this work is the application of a novel geologic-model parameterization and history-matching work flow for modeling of a mature oil field with decades of production history, and which is currently being developed with CO2 EOR.