We present a novel two-stage Markov Chain Monte Carlo (MCMC) method that improves the efficiency of MCMC sampling while maintaining its sampling rigor. Our method employs response surfaces as surrogate models in the first stage to direct the sampling and identify promising reservoir models, replacing computationally expensive multiphase flow simulations. In the second stage, flow simulations are conducted only on proposals that pass the first stage to calculate acceptance probability, and the surrogate model is updated regularly upon adding new flow simulations. This strategy significantly increases the acceptance rate and reduces computational costs compared to conventional MCMC sampling, without sacrificing accuracy. To demonstrate the efficacy and efficiency of our approach, we apply it to a field example involving three-phase flow and the integration of historical reservoir production data, generating multiple reservoir models and assessing uncertainty in production forecasts.
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Acknowledgements
This research was funded by the National Natural Science Foundation of China (Grant Nos. 51974253, 51934005, 52004219), the Natural Science Basic Research Program of Shaanxi (Grant Nos. 2017JM5109 and 2020JQ-781), the Scientific Research Program Funded by Education Department of Shaanxi Province (Grant Nos. 18JS085 and 20JS117), and the Graduate Student Innovation and Practical Ability Training Program of Xi’an Shiyou University (Grant No. YCS21211018).
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Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 2, pp. 134–137 March– April, 2023.
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Ma, X., Pan, X., Zhan, J. et al. Two-Stage MCMC with Surrogate Models for Efficient Uncertainty Quantification in Multiphase Flow. Chem Technol Fuels Oils 59, 420–427 (2023). https://doi.org/10.1007/s10553-023-01541-5
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DOI: https://doi.org/10.1007/s10553-023-01541-5