Lattice Boltzmann flow simulations with applications of reduced order modeling techniques
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...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/51336 |
| _version_ | 1848758671876554752 |
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| author | Brown, D. Li, J. Calo, Victor Ghommem, M. Efendiev, Y. |
| author_facet | Brown, D. Li, J. Calo, Victor Ghommem, M. Efendiev, Y. |
| author_sort | Brown, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. Copyright 2014, International Petroleum Technology Conference. |
| first_indexed | 2025-11-14T09:47:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-51336 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:47:42Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-513362017-09-13T21:24:56Z Lattice Boltzmann flow simulations with applications of reduced order modeling techniques Brown, D. Li, J. Calo, Victor Ghommem, M. Efendiev, Y. 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. Copyright 2014, International Petroleum Technology Conference. 2014 Conference Paper http://hdl.handle.net/20.500.11937/51336 10.2523/IPTC-17457-MS restricted |
| spellingShingle | Brown, D. Li, J. Calo, Victor Ghommem, M. Efendiev, Y. Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title | Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title_full | Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title_fullStr | Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title_full_unstemmed | Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title_short | Lattice Boltzmann flow simulations with applications of reduced order modeling techniques |
| title_sort | lattice boltzmann flow simulations with applications of reduced order modeling techniques |
| url | http://hdl.handle.net/20.500.11937/51336 |