Model calibration and optimization of a post-combustion CO2 WAG pilot in a mature oil field

• Published in: Fuel (Guildford) Vol. 255; no. C
• Main Authors: Olalotiti-Lawal, Feyi, Onishi, Tsubasa, Datta-Gupta, Akhil, Fujita, Yusuke, Watanabe, Daiki, Hagiwara, Kenji
• Format: Journal Article
• Language: English
• Published: United States Elsevier 01.11.2019
• Subjects: 02 PETROLEUM; CO2 enhanced oil recovery; design of experiment; model calibration; WAG optimization
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2019.115810

A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world's largest postcombustion CO2 capture and utilization initiative. With a fluvial formation geology, this is a challenging and novel application of CO2 enhanced oil recovery. The aim of this study is two-fold. First, to build a predictive dynamic model that incorporates the data acquired during the pilot operation. Second, to utilize this model to provide guidance for optimal design for a field-wide CO2 EOR operation. Our study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. Next, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. Sensitivity studies were conducted using the calibrated pilot model to evaluate the effects of various operational parameters. Finally, we performed a Design of Experiment based multi-objective optimization of the CO2 WAG processes using the influential operational parameters identified from the sensitivity analysis.