Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow

Thilo Strauss; Xiaolin Fan; Shuyu Sun; Taufiquar Khan

doi:10.1016/j.procs.2015.05.291

Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow

Thilo Strauss, Xiaolin Fan, Shuyu Sun, Taufiquar Khan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and fp (0 < p δ 2) regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem. We present simulations to estimate the distribution for each pixel for the image reconstruction of the absolute permeability.

Original language	English (US)
Title of host publication	Procedia Computer Science
Publisher	Elsevier BV
Pages	1188-1197
Number of pages	10
DOIs	https://doi.org/10.1016/j.procs.2015.05.291
State	Published - Jun 1 2015

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10.1016/j.procs.2015.05.291

https://repository.kaust.edu.sa/bitstream/10754/556696/1/1-s2.0-S1877050915010996-main.pdf

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title = "Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow",

abstract = "In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and fp (0 < p δ 2) regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem. We present simulations to estimate the distribution for each pixel for the image reconstruction of the absolute permeability.",

author = "Thilo Strauss and Xiaolin Fan and Shuyu Sun and Taufiquar Khan",

note = "KAUST Repository Item: Exported on 2020-10-01",

year = "2015",

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doi = "10.1016/j.procs.2015.05.291",

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AU - Strauss, Thilo

AU - Fan, Xiaolin

AU - Sun, Shuyu

AU - Khan, Taufiquar

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2015/6/1

Y1 - 2015/6/1

N2 - In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and fp (0 < p δ 2) regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem. We present simulations to estimate the distribution for each pixel for the image reconstruction of the absolute permeability.

AB - In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and fp (0 < p δ 2) regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem. We present simulations to estimate the distribution for each pixel for the image reconstruction of the absolute permeability.

UR - http://hdl.handle.net/10754/556696

UR - http://linkinghub.elsevier.com/retrieve/pii/S1877050915010996

UR - http://www.scopus.com/inward/record.url?scp=84939197540&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2015.05.291

DO - 10.1016/j.procs.2015.05.291

M3 - Conference contribution

SP - 1188

EP - 1197

BT - Procedia Computer Science

PB - Elsevier BV

ER -

Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow

Abstract

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