scholarly journals A New Upscaling Method for Fluid Flow Simulation in Highly Heterogeneous Unconventional Reservoirs

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Qi Zhang ◽  
Huibin Yu ◽  
Xiaofeng Li ◽  
Tiesheng Liu ◽  
Junfeng Hu
Keyword(s):  
Fluid Flow ◽  
Flow Simulation ◽  
Heterogeneous Porous Media ◽  
Unconventional Reservoirs ◽  
Pore Network Model ◽  
Transport Mechanisms ◽  
Flow Properties ◽  
Computational Capability ◽  
Flow Mechanisms ◽  
Boltzmann Method

High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study proposed a new upscaling method integrated gray lattice Boltzmann method (GLBM) and pore network model (PNM), accounting for the fluid flow in heterogeneous porous media. This method can reasonably reduce the computational loads while preserving certain micropore characteristics. Verifications are conducted by comparing the simulation and experimental results on tight sandstones, and good agreements are achieved. The proposed method is proven to be capable of estimating bulk properties in highly heterogenous unconventional reservoirs. This method could contribute to the development of multiscale pore structure characterizations and enhance the understandings of fluid flow mechanisms in unconventional reservoirs.

scholarly journals Fluid flow simulation on the Cell Broadband Engine using the lattice Boltzmann method

2009 ◽  
Vol 58 (5) ◽  
pp. 1062-1070 ◽  
Author(s):  
Markus Stürmer ◽  
Jan Götz ◽  
Gregor Richter ◽  
Arnd Dörfler ◽  
Ulrich Rüde
Keyword(s):  
Fluid Flow ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Flow Simulation ◽  
Cell Broadband Engine ◽  
Boltzmann Method

Imbibition and Water Blockage In Unconventional Reservoirs: Well-Management Implications During Flowback and Early Production

2014 ◽  
Vol 17 (04) ◽  
pp. 497-506 ◽  
Author(s):  
A.. Bertoncello ◽  
J.. Wallace ◽  
C.. Blyton ◽  
M.. Honarpour ◽  
C.S.. S. Kabir
Keyword(s):  
Flow Simulation ◽  
Unconventional Reservoirs ◽  
Water Production ◽  
Pressure Data ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Water Blocking ◽  
Flow Mechanisms ◽  
Early Production ◽  
Resting Period

Summary Driven by field logistics in an unconventional setting, a well may undergo weeks to months of shut-in after hydraulic-fracture stimulation. In unconventional reservoirs, field experiences indicate that such shut-in episodes may improve well productivity significantly while reducing water production. Multiphase-flow mechanisms were found to explain this behavior. Aided by laboratory relative permeability and capillary pressure data, and their dependency on stress in a shale-gas reservoir, the flow-simulation model was able to reproduce the suspected water-blocking behavior. Results demonstrate that a well-resting period improves early productivity and reduces water production. The results also indicate that minimizing water invasion in the formation is crucial to avoid significant water blockage.

scholarly journals Lattice Boltzmann Simulation of Permeability and Tortuosity for Flow through Dense Porous Media

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ping Wang
Keyword(s):  
Porous Media ◽  
Fluid Flow ◽  
Lattice Boltzmann ◽  
Flow Direction ◽  
Flow Simulation ◽  
The Body ◽  
Lattice Boltzmann Simulation ◽  
Sphere Radius ◽  
The Media ◽  
Boltzmann Method

Discrete element method (DEM) is used to produce dense and fixed porous media with rigid mono spheres. Lattice Boltzmann method (LBM) is adopted to simulate the fluid flow in interval of dense spheres. To simulating the same physical problem, the permeability is obtained with different lattice number. We verify that the permeability is irrelevant to the body force and the media length along flow direction. The relationships between permeability, tortuosity and porosity, and sphere radius are researched, and the results are compared with those reported by other authors. The obtained results indicate that LBM is suited to fluid flow simulation of porous media due to its inherent theoretical advantages. The radius of sphere should have ten lattices at least and the media length along flow direction should be more than twenty radii. The force has no effect on the coefficient of permeability with the limitation of slow fluid flow. For mono spheres porous media sample, the relationship of permeability and porosity agrees well with the K-C equation, and the tortuosity decreases linearly with increasing porosity.

scholarly journals Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Serveh Kamrava ◽  
Muhammad Sahimi ◽  
Pejman Tahmasebi
Keyword(s):  
Porous Media ◽  
Fluid Flow ◽  
Stokes Equations ◽  
Computation Time ◽  
Mass Conservation ◽  
Biological Tissues ◽  
Heterogeneous Porous Media ◽  
Navier Stokes ◽  
Time Interval ◽  
Flow Properties

AbstractFluid flow in heterogeneous porous media arises in many systems, from biological tissues to composite materials, soil, wood, and paper. With advances in instrumentations, high-resolution images of porous media can be obtained and used directly in the simulation of fluid flow. The computations are, however, highly intensive. Although machine learning (ML) algorithms have been used for predicting flow properties of porous media, they lack a rigorous, physics-based foundation and rely on correlations. We introduce an ML approach that incorporates mass conservation and the Navier–Stokes equations in its learning process. By training the algorithm to relatively limited data obtained from the solutions of the equations over a time interval, we show that the approach provides highly accurate predictions for the flow properties of porous media at all other times and spatial locations, while reducing the computation time. We also show that when the network is used for a different porous medium, it again provides very accurate predictions.

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