“HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model”

Authors: Kushila Jayamanne and Bernt Lie,
Affiliation: University of South-Eastern Norway
Reference: 2023, Vol 44, No 1, pp. 17-29.

Keywords: Parameter estimation, Markov Chain Monte Carlo, Model uncertainty, Hamiltonian Monte Carlo, No-U-Turn Sampler

Abstract: Parametric model uncertainties could have a high impact on the predictive capabilities of a model. When process measurements become available, these uncertainties may be reduced using parameter estimation techniques. Estimation techniques founded on the Bayesian framework in particular are powerful: they produce a probability density function (PDF) of the estimated parameter rather than a single point estimate. In this paper, we consider a gas lifted oil field model whose predictions are highly sensitive to uncertainty in its parameters. We apply Markov Chain Monte Carlo (MCMC) methods, which follow the Bayesian paradigm, to estimate these parameters, and thereby reduce the uncertainty in the model predictions; two different algorithms, Hamiltonian Monte Carlo (HMC) and No-U-Turn Sampler (NUTS), are used. The probabilistic programming language (PPL), Turing in Julia is used for implementation. Monte Carlo simulations and/or data retrodiction is performed prior to and post parameter estimation, to evaluate the uncertainty in model predictions; the outcomes are compared to determine the efficacy of parameter estimation. Results show that the computed posterior distributions yield model predictions that are in close agreement with the observations, and that model uncertainty is effectively reduced.

PDF PDF (4788 Kb)        DOI: 10.4173/mic.2023.1.2

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  title={{HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model}},
  author={Jayamanne, Kushila and Lie, Bernt},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}