Comparison of stochastic sampling algorithms for uncertainty quantification

L. Mohamed, M. Christie, V. Demyanov

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    30 Citations (Scopus)

    Abstract

    History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir - for example from analogue outcrop data - and update our reservoir models with observations, for example from production data or time lapse seismic. The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history matched models is the sampling algorithm used to generate the models. Algorithms that have been studied include gradient methods, genetic algorithms, the Ensemble Kalman Filter, and others. This paper investigates the efficiency of three stochastic sampling algorithms: Hamiltonian Monte Carlo (HMC) algorithm, Particle Swarm Optimization (PSO) algorithm and the Neighborhood Algorithm (NA). HMC is a Markov Chain Monte Carlo (MCMC) technique that uses Hamiltonian dynamics to achieve larger jumps than are possible with other MCMC techniques. PSO is a swarm intelligence algorithm that uses similar dynamics to HMC to guide the search, but incorporates acceleration and damping parameters to provide rapid convergence to possible multiple minima. The Neighbourhood Algorithm is a sampling technique that uses the properties of Voronoi cells in high dimensions to achieve multiple history matched models. The algorithms are compared by generating multiple history matched reservoir models, and comparing the plO - p. 50 - p. 90 uncertainty bounds produced by each algorithm. We show that all the algorithms are able to find equivalent match qualities for this example, but that some algorithms are able to find good fitting results quickly, whereas others are able to find a more diverse set of models in parameter space. The effects of the different sampling of model parameter space are compared in terms of the p. 10 - p. 50 - p. 90 uncertainty bounds in forecast oil rate. These results show that algorithms based on Hamiltonian dynamics and swarm intelligence concepts have the potential to be effective tools in uncertainty quantification in the oil industry. Copyright 2009, Society of Petroleum Engineers.

    Original languageEnglish
    Title of host publicationSPE Reservoir Simulation Symposium Proceedings
    Pages993-1004
    Number of pages12
    Volume2
    DOIs
    Publication statusPublished - Feb 2009
    EventSPE Reservoir Simulation Symposium 2009 - The Woodlands, TX, United States
    Duration: 2 Feb 20094 Feb 2009

    Conference

    ConferenceSPE Reservoir Simulation Symposium 2009
    CountryUnited States
    CityThe Woodlands, TX
    Period2/02/094/02/09

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  • Cite this

    Mohamed, L., Christie, M., & Demyanov, V. (2009). Comparison of stochastic sampling algorithms for uncertainty quantification. In SPE Reservoir Simulation Symposium Proceedings (Vol. 2, pp. 993-1004) https://doi.org/doi:10.2118/119139-MS