Bayesian inversion for steady flow in fractured porous media with contact on fractures and hydro mechanical coupling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F20%3A00533163" target="_blank" >RIV/68145535:_____/20:00533163 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/20:10244847

Result on the web

<a href="https://link.springer.com/article/10.1007/s10596-020-09935-8" target="_blank" >https://link.springer.com/article/10.1007/s10596-020-09935-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10596-020-09935-8" target="_blank" >10.1007/s10596-020-09935-8</a>

Result language

angličtina

Original language name

Bayesian inversion for steady flow in fractured porous media with contact on fractures and hydro mechanical coupling

Original language description

The paper is motivated by a strong interest in numerical analysis of flow in fractured porous media, e.g., rocks in geo-engineering applications. It follows the conception of porous media as a continuum with fractures which are represented as lower dimensional objects. In the paper, the finite element discretization of the flow in coupled continuum and fractures is used. Fluid pressures serve as the basic unknowns. In many applications, the flow is connected with deformations of the porous matrix, therefore, the hydro-mechanical coupling is also considered. The fluid pressure is transferred to the mechanical load in both pores and fractures and the considered mechanical model involves elastic deformations of the porous matrix and opening/closing of the fractures with the non-penetration constraint. The mechanical model with this constraint is implemented via the technique of the Lagrange multipliers, duality formulation, and combination with a suitable domain decomposition method. There is usually lack of information about problem parameters and they undergo many uncertainties coming e.g. from the heterogeneity of rock formations and complicated realization of experiments for parameter identification. These experiments rarely provide some of the asked parameters directly but require solving inverse problems. The stochastic (Bayesian) inversion is natural due to the mentioned uncertainties. In this paper, the implementation of the Bayesian inversion is realized via Metropolis-Hastings Markov chain Monte Carlo approach. For the reduction of computational demands, the sampling procedure uses the delayed acceptance of samples based on a surrogate model which is constructed during a preliminary sampling process. The developed hydro-mechanical model and the implemented Bayesian inversion are tested on two types of model inverse problems.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10102 - Applied mathematics

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computational Geosciences

ISSN

1420-0597

e-ISSN

—

Volume of the periodical

24

Issue of the periodical within the volume

February 2020

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

22

Pages from-to

1911-1932

UT code for WoS article

000517019300001

EID of the result in the Scopus database

2-s2.0-85081326146