Nested sampling algorithm for subsurface flow model selection, uncertainty quantification and nonlinear calibration

Ahmed H. ELsheikh, Mary F Wheeler, Ibrahim Hoteit

    Research output: Contribution to journalArticle

    Abstract

    Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface ow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data-mismatch of the calibrated model.
    Original languageEnglish
    Pages (from-to)8383-8399
    Number of pages17
    JournalWater Resources Research
    Volume49
    Issue number12
    Early online date7 Nov 2013
    DOIs
    Publication statusPublished - Dec 2013

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    subsurface flow
    calibration
    sampling
    hydrocarbon reservoir
    parameterization
    remediation
    aquifer
    groundwater
    pollutant

    Cite this

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    abstract = "Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface ow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data-mismatch of the calibrated model.",
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    Nested sampling algorithm for subsurface flow model selection, uncertainty quantification and nonlinear calibration. / ELsheikh, Ahmed H. ; Wheeler, Mary F; Hoteit, Ibrahim.

    In: Water Resources Research, Vol. 49, No. 12, 12.2013, p. 8383-8399.

    Research output: Contribution to journalArticle

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