Lattice Boltzmann flow simulations with applications of reduced order modeling techniques

Handle URI:
http://hdl.handle.net/10754/564846
Title:
Lattice Boltzmann flow simulations with applications of reduced order modeling techniques
Authors:
Brown, Donald; Li, Jun; Calo, Victor M. ( 0000-0002-1805-4045 ) ; Ghommem, Mehdi; Efendiev, Yalchin R.
Abstract:
With the recent interest in shale gas, an understanding of the flow mechanisms at the pore scale and beyond is necessary, which has attracted a lot of interest from both industry and academia. One of the suggested algorithms to help understand flow in such reservoirs is the Lattice Boltzmann Method (LBM). The primary advantage of LBM is its ability to approximate complicated geometries with simple algorithmic modificatoins. In this work, we use LBM to simulate the flow in a porous medium. More specifically, we use LBM to simulate a Brinkman type flow. The Brinkman law allows us to integrate fast free-flow and slow-flow porous regions. However, due to the many scales involved and complex heterogeneities of the rock microstructure, the simulation times can be long, even with the speed advantage of using an explicit time stepping method. The problem is two-fold, the computational grid must be able to resolve all scales and the calculation requires a steady state solution implying a large number of timesteps. To help reduce the computational complexity and total simulation times, we use model reduction techniques to reduce the dimension of the system. In this approach, we are able to describe the dynamics of the flow by using a lower dimensional subspace. In this work, we utilize the Proper Orthogonal Decomposition (POD) technique, to compute the dominant modes of the flow and project the solution onto them (a lower dimensional subspace) to arrive at an approximation of the full system at a lowered computational cost. We present a few proof-of-concept examples of the flow field and the corresponding reduced model flow field.
KAUST Department:
Applied Mathematics and Computational Science Program; Biological and Environmental Sciences and Engineering (BESE) Division; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Numerical Porous Media SRI Center (NumPor)
Publisher:
Society of Petroleum Engineers (SPE)
Journal:
International Petroleum Technology Conference
Conference/Event name:
International Petroleum Technology Conference 2014: Unlocking Energy Through Innovation, Technology and Capability, IPTC 2014
Issue Date:
2014
DOI:
10.2523/17457-ms
Type:
Conference Paper
Appears in Collections:
Conference Papers; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBrown, Donalden
dc.contributor.authorLi, Junen
dc.contributor.authorCalo, Victor M.en
dc.contributor.authorGhommem, Mehdien
dc.contributor.authorEfendiev, Yalchin R.en
dc.date.accessioned2015-08-04T07:22:56Zen
dc.date.available2015-08-04T07:22:56Zen
dc.date.issued2014en
dc.identifier.doi10.2523/17457-msen
dc.identifier.urihttp://hdl.handle.net/10754/564846en
dc.description.abstractWith the recent interest in shale gas, an understanding of the flow mechanisms at the pore scale and beyond is necessary, which has attracted a lot of interest from both industry and academia. One of the suggested algorithms to help understand flow in such reservoirs is the Lattice Boltzmann Method (LBM). The primary advantage of LBM is its ability to approximate complicated geometries with simple algorithmic modificatoins. In this work, we use LBM to simulate the flow in a porous medium. More specifically, we use LBM to simulate a Brinkman type flow. The Brinkman law allows us to integrate fast free-flow and slow-flow porous regions. However, due to the many scales involved and complex heterogeneities of the rock microstructure, the simulation times can be long, even with the speed advantage of using an explicit time stepping method. The problem is two-fold, the computational grid must be able to resolve all scales and the calculation requires a steady state solution implying a large number of timesteps. To help reduce the computational complexity and total simulation times, we use model reduction techniques to reduce the dimension of the system. In this approach, we are able to describe the dynamics of the flow by using a lower dimensional subspace. In this work, we utilize the Proper Orthogonal Decomposition (POD) technique, to compute the dominant modes of the flow and project the solution onto them (a lower dimensional subspace) to arrive at an approximation of the full system at a lowered computational cost. We present a few proof-of-concept examples of the flow field and the corresponding reduced model flow field.en
dc.publisherSociety of Petroleum Engineers (SPE)en
dc.titleLattice Boltzmann flow simulations with applications of reduced order modeling techniquesen
dc.typeConference Paperen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentNumerical Porous Media SRI Center (NumPor)en
dc.identifier.journalInternational Petroleum Technology Conferenceen
dc.conference.date19 January 2014 through 22 January 2014en
dc.conference.nameInternational Petroleum Technology Conference 2014: Unlocking Energy Through Innovation, Technology and Capability, IPTC 2014en
dc.conference.locationDohaen
dc.contributor.institutionDepartment of Mathematics, ISC, Texas A and M University, College Station, TX, United Statesen
kaust.authorBrown, Donalden
kaust.authorCalo, Victor M.en
kaust.authorGhommem, Mehdien
kaust.authorLi, Junen
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