Multi‐GPU solution to the lattice Boltzmann method: An application in multiscale digital rock simulation for shale formation

Characterization of rock properties is vital in producing oil and gas from shale reservoirs in an economically viable fashion. The nano‐pore structure and ultralow permeability in shale reservoirs present challenges to the traditional experimental characterization methods. Digital rock physics for the estimation of rock properties, especially for shale reservoirs, has become a powerful tool that greatly complements to lab experiments by combining advance imaging techniques with numerical simulations. The lattice Boltzmann method (LBM) is a well‐applied numerical method to simulate the fluid flow in pore structures at multiple length scales. Usually, the LBM simulation is resource intense because of its computation complexity and is facing great numerical challenges in extremely large‐cale computation. In this paper, we propose a multi‐GPU parallel implementation of 3D LBM on a hybrid high‐performance computing cluster to perform large‐scale simulations in reconstructed digital rocks. The program provides multiscale solution, pore scale and representative elementary volume (REV) scale based on the resolution of digital rock images. Optimization strategies are applied on partitioning simulation domain, improving data communication efficiency and maximizing CUDA occupancy. When running on a cluster of 32 GPUs, the proposed parallel implementation achieves a speedup of 1074x comparing to the in‐house sequential program.

[1]  Azra N. Tutuncu,et al.  Real Time Monitoring of Permeability, Elastic Moduli and Strength in Sands and Shales Using Digital Rock Physics , 2003 .

[2]  Erik H. Saenger,et al.  Digital Rock Physics: Numerical Vs. Laboratory Measurements , 2011 .

[3]  Tianluo Chen,et al.  Simulation of Shale Gas Transport in 3D Complex Nanoscale-Pore Structures Using the Lattice Boltzmann Method , 2015 .

[4]  Joel Walls,et al.  Rock Property Determination Using Digital Rock Physics , 2003 .

[5]  Nobuyuki Satofuka,et al.  Simulation of turbulent flow by lattice Boltzmann method and conventional method on a GPU , 2013 .

[6]  Bernard Tourancheau,et al.  A new approach to the lattice Boltzmann method for graphics processing units , 2011, Comput. Math. Appl..

[7]  Takayuki Aoki,et al.  Multi-GPU performance of incompressible flow computation by lattice Boltzmann method on GPU cluster , 2011, Parallel Comput..

[8]  Saad Matar,et al.  Petrophyscial and Fluid Flow Properties of a Tight Carbonate Source Rock Using Digital Rock Physics , 2015 .

[9]  Qisu Zou,et al.  N ov 1 99 6 On pressure and velocity flow boundary conditions and bounceback for the lattice Boltzmann BGK model , 2008 .

[10]  G. Qin,et al.  Numerical modeling of slippage and adsorption effects on gas transport in shale formations using the lattice Boltzmann method , 2015 .

[11]  Shiyi Chen,et al.  LATTICE BOLTZMANN METHOD FOR FLUID FLOWS , 2001 .

[12]  Q. Zou,et al.  On pressure and velocity boundary conditions for the lattice Boltzmann BGK model , 1995, comp-gas/9611001.

[13]  Raffaele Tripiccione,et al.  Massively parallel lattice-Boltzmann codes on large GPU clusters , 2016, Parallel Comput..

[14]  G. Qin,et al.  Upscaling in Numerical Simulation of Shale Transport Properties by Coupling Molecular Dynamics Simulation with Lattice Boltzmann Method , 2016 .

[15]  Zhaoli Guo,et al.  Lattice Boltzmann model for incompressible flows through porous media. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  G. Qin,et al.  A Rigorous Upscaling Procedure to Predict Macro-scale Transport Properties of Natural Gas in Shales by Coupling Molecular Dynamics with Lattice Boltzmann Method , 2016 .

[17]  Bernard Tourancheau,et al.  Multi-GPU implementation of the lattice Boltzmann method , 2013, Comput. Math. Appl..

[18]  B. Shi,et al.  Discrete lattice effects on the forcing term in the lattice Boltzmann method. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Nikolai E. Grachev Digital core analysis - the future of petrophysics , 2012 .

[20]  Bernard Tourancheau,et al.  Scalable lattice Boltzmann solvers for CUDA GPU clusters , 2013, Parallel Comput..

[21]  G. Qin,et al.  Permeability Prediction Considering Surface Diffusion for Gas Shales by Lattice Boltzmann Simulations on Multi-Scale Reconstructed Digital Rocks , 2016 .

[22]  Hao Zhou,et al.  GPU implementation of lattice Boltzmann method for flows with curved boundaries , 2012 .

[23]  M. Januszewski,et al.  Sailfish: A flexible multi-GPU implementation of the lattice Boltzmann method , 2013, Comput. Phys. Commun..

[24]  Y. Qian,et al.  Lattice BGK Models for Navier-Stokes Equation , 1992 .

[25]  Nikolai E. Grachev Digital Core Analysis -The Future of Petrophysics (Russian) , 2012 .

[26]  George E. Karniadakis,et al.  GPU-accelerated red blood cells simulations with transport dissipative particle dynamics , 2017, Comput. Phys. Commun..

[27]  Dirk Ribbrock,et al.  A simulation suite for Lattice-Boltzmann based real-time CFD applications exploiting multi-level parallelism on modern multi- and many-core architectures , 2011, J. Comput. Sci..

[28]  A. Louisa,et al.  コロイド混合体における有効力 空乏引力から集積斥力へ | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 2002 .