Abstract
In enhanced oil recovery (EOR) processes, foam injection reduces gas mobility and increases apparent viscosity, thus increasing recovery efficiency. The quantification of uncertainty is essential in developing and evaluating mathematical models. In this work, we perform uncertainty quantification (UQ) of two-phase flow models for foam injection using the STARS model with data from a series of foam quality-scan experiments. We first performed the parameter estimation based on three datasets of foam quality-scans on Indiana limestone carbonate core samples. Then distributions of the parameters are inferred via the Markov Chain Monte Carlo method (MCMC). This approach allows propagating parametric uncertainty to the STARS apparent viscosity model. In particular, the framework for UQ allowed us to identify how the lack of experimental data affected the reliability of the calibrated models.
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Acknowledgements
The authors thank Dr. Ivan Landin for suggestions on improving the manuscript.
This research was carried out in association with the ongoing R&D projects ANP number 20358-8, “Desenvolvimento de formulações contendo surfactantes e nanopartículas para controle de mobilidade de gás usando espumas para recuperação avançada de petróleo” (PUC-Rio/Shell Brasil/ANP) and ANP number 201715-9, “Modelagem matemática e computacional de injeção de espuma usa em recuperação de petróleo” (UFJF/Shell Brazil/ANP) sponsored by Shell Brasil under the ANP R&D levy as “Compromisso de Investimentos com Pesquisa e Desenvolvimento”, in partnership with Petrobras.
G.C. was supported in part by CNPq grant 303245/2019-0 and FAPEMIG grant APQ-00405-21. B. M. R. was supported in part by CNPq grant 310722/2021-7.
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de Miranda, G.B. et al. (2022). Characterization of Foam-Assisted Water-Gas Flow via Inverse Uncertainty Quantification Techniques. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_26
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