scholarly journals Development of a Scalable Thermal Reservoir Simulator on Distributed-Memory Parallel Computers

Fluids ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 395
Author(s):  
Hui Liu ◽  
Zhangxin Chen ◽  
Xiaohu Guo ◽  
Lihua Shen
Keyword(s):  
Numerical Methods ◽  
Large Scale ◽  
Distributed Memory ◽  
Parallel Implementation ◽  
Parallel Computers ◽  
Engineering Industry ◽  
Petroleum Engineering ◽  
Flow Equations ◽  
Thermal Reservoir ◽  
Reservoir Simulator

Reservoir simulation is to solve a set of fluid flow equations through porous media, which are partial differential equations from the petroleum engineering industry and described by Darcy’s law. This paper introduces the model, numerical methods, algorithms and parallel implementation of a thermal reservoir simulator that is designed for numerical simulations of a thermal reservoir with multiple components in three-dimensional domain using distributed-memory parallel computers. Its full mathematical model is introduced with correlations for important properties and well modeling. Efficient numerical methods (discretization scheme, matrix decoupling methods, and preconditioners), parallel computing technologies, and implementation details are presented. The numerical methods applied in this paper are suitable for large-scale thermal reservoir simulations with dozens of thousands of CPU cores (MPI processes), which are efficient and scalable. The simulator is designed for giant models with billions or even trillions of grid blocks using hundreds of thousands of CPUs, which is our main focus. The validation part is compared with CMG STARS, which is one of the most popular and mature commercial thermal simulators. Numerical experiments show that our results match commercial simulators, which confirms the correctness of our methods and implementations. SAGD simulation with 7406 well pairs is also presented to study the effectiveness of our numerical methods. Scalability testings demonstrate that our simulator can handle giant models with billions of grid blocks using 100,800 CPU cores and the simulator has good scalability.

scholarly journals Efficient Simulations of Large Scale Convective Heat Transfer Problems

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Damian Goik ◽  
Krzysztof Banaś ◽  
Jan Bielański ◽  
Kazimierz Chłoń
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Large Scale ◽  
Stokes Equations ◽  
Parallel Implementation ◽  
Navier Stokes ◽  
Flow Equations ◽  
Transfer Problems

We describe an approach for efficient solution of large scale convective heat transfer problems, formulated as coupled unsteady heat conduction and incompressible fluid flow equations. The original problem is discretized in time using classical implicit methods, while stabilized finite elements are used for space discretization. The algorithm employed for the discretization of the fluid flow problem uses Picard's iterations to solve the arising nonlinear equations. Both problems, heat transfer and Navier-Stokes quations, give rise to large sparse systems of linear equations. The systems are solved using iterative GMRES solver with suitable preconditioning. For the incompressible flow equations we employ a special preconditioner based on algebraic multigrid (AMG) technique. The paper presents algorithmic and implementation details of the solution procedure, which is suitably tuned, especially for ill conditioned systems arising from discretizations of incompressible Navier-Stokes equations. We describe parallel implementation of the solver using MPI and elements of PETSC library. The scalability of the solver is favourably compared with other methods such as direct solvers and standard GMRES method with ILU preconditioning.  

A Parallel Thermal Reservoir Simulator on Distributed-Memory Supercomputers

2016 ◽  
Author(s):  
He Zhong ◽  
Hui Liu ◽  
Tao Cui ◽  
Kun Wang ◽  
Bo Yang ◽  
...  
Keyword(s):  
Distributed Memory ◽  
Thermal Reservoir ◽  
Reservoir Simulator

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

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