About: Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface
Creator	Al-Mamun, Abdullah Pereira, Felipe Ali, Alsadig Rahunanthan, Arunasalam
topic	covid:b428b67cbb25184a8b9f7d38c3f234024b3a9218#this
Source	PMC
abstract	Accurate predictions in subsurface flows require the forecasting of quantities of interest by applying models of subsurface fluid flow with very little available data. In general a Bayesian statistical approach along with a Markov Chain Monte Carlo (MCMC) algorithm can be used for quantifying the uncertainties associated with subsurface parameters. However, the complex nature of flow simulators presents considerable challenges to accessing inherent uncertainty in all flow simulator parameters of interest. In this work we focus on the transport of contaminants in a heterogeneous permeability field of a aquifer. In our problem the limited data comes in the form of contaminant fractional flow curves at monitoring wells of the aquifer. We then employ a Karhunen-Loève expansion to truncate the stochastic dimension of the permeability field and thus the expansion helps reducing the computational burden. Aiming to reduce the computational burden further, we code our numerical simulator using parallel programming procedures on Graphics Processing Units (GPUs). In this paper we mainly present a comparative study of two well-known MCMC methods, namely, two-stage and DiffeRential Evolution Adaptive Metropolis (DREAM), for the characterization of the two-dimensional aquifer. A thorough statistical analysis of ensembles of the contaminant fractional flow curves from both MCMC methods is presented. The analysis indicates that although the average fractional flow curves are quite similar, both time-dependent ensemble variances and posterior analysis are considerably distinct for both methods.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50436-6_56
has license	no-cc
sha1sum (hex)	b428b67cbb25184a8b9f7d38c3f234024b3a9218
schema:url	https://doi.org/10.1007/978-3-030-50436-6_56
resource representing a document's title	Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface
has PubMed Central identifier	PMC7304772
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:b428b67cbb25184a8b9f7d38c3f234024b3a9218#body_text
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is schema:about of	named entity 'presents' named entity 'employ' named entity 'general' named entity 'forecasting' named entity 'problem' named entity 'PROCEDURES' named entity 'CODE' named entity 'AVERAGE' named entity 'PROGRAMMING' named entity 'ACCURATE' named entity 'Units' named entity 'contaminants' named entity 'heterogeneous' named entity 'two-dimensional' named entity 'simulator' named entity 'subsurface' named entity 'models' named entity 'outlier' named entity 'covariance-matrix' named entity 'MCMC' named entity 'GPU' named entity 'limiting distribution' named entity 'sampling' named entity 'computational modeling' named entity 'algorithm' named entity 'Algorithm' named entity 'vector' named entity 'KLE' named entity 'MCMC' named entity 'KLE' named entity 'MCMC' named entity 'probability' named entity 'ith' named entity 'KLE' named entity 'randomly chosen' named entity 'Darcy's law' named entity 'variance analysis' named entity 'outflow boundary' named entity 'GPUs' named entity 'posterior densities' named entity 'MCMC' named entity 'algorithm' named entity 'numerical solution' named entity 'absolute permeability' named entity 'MCMC' named entity 'algorithm' named entity 'Bayesian' named entity 'MCMC' named entity 'ensembles' named entity 'simulator' named entity 'flow' named entity 'DREAM' named entity 'computational burden' named entity 'fluid flow' named entity 'MCMC' named entity 'computational burden' named entity 'Graphics Processing Units' named entity 'Markov Chain Monte Carlo' named entity 'DiffeRential Evolution' named entity 'Fluid Flow' named entity 'GPUs' named entity 'transport' named entity 'probability' named entity 'aquifer' named entity 'tuning parameter' named entity 'genetic algorithm' named entity 'PVI' named entity 'MCMC' named entity 'identity matrix' named entity 'MCMC' named entity 'algorithm' named entity 'velocity field' named entity 'numerical scheme' named entity 'MCMC' named entity 'eigenvalue' named entity 'sample data' named entity 'partial differential equations' named entity 'KLE'

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