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

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.

Practical Wavelet 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.

Parallel iteration of two-step Runge-Kutta methods

In this paper, we introduce the Parallel iteration of two-step Runge-Kutta methods for solving non-stiff initial-value problems for systems of first-order differential equations (ODEs): y′(t) = f(t, y(t)), for use on parallel computers. Starting with an s−stage implicit two-step Runge-Kutta (TSRK) method of order p, we apply the highly parallel predictor-corrector iteration process in P (EC)mE mode. In this way, we obtain an explicit two-step Runge-Kutta method that has order p for all m, and that requires s(m+1) right-hand side evaluations per step of which each s evaluation can be computed parallelly. By a number of numerical experiments, we show the superiority of the parallel predictor-corrector methods proposed in this paper over both sequential and parallel methods available in the literature.

Coarse Grained Parallel Selection

Several efficient, but non-optimal, solutions to the Selection Problem on coarse grained parallel computers have appeared in the literature. We consider the example of the Saukas-Song algorithm; we analyze it without expressing the running time in terms of communication rounds. This shows that while in the best case the Saukas-Song algorithm runs in asymptotically optimal time, in general it does not. We propose another algorithm for coarse grained selection that has optimal expected running time.

STUDY ON SCOURING IN PILE OF OFFSHORE WIND FARMING BY NUMERICAL ANALYSIS USING BUILDING-CUBE METHOD

In recent years, offshore wind farming has spread all over the world, and there has been rapid growth of not only conventional onshore wind farming but also offshore wind farming in the sea. However, there are many problems to be solved in offshore wind farming. Among them, the scour of the ocean floor caused by wave and tide has a great influence on the support structure. The purpose of this study is to clarify the scour phenomenon from the flow field and vortex generation conditions by numerical simulation using Building Cube Method. We conducted the simulation of the experiment which handles scouring around the monopile performed by Chen et al. (2018). The Building Cube Method was developed by Nakahashi and Kim (2004). In this study, we used CADMAS-BCM (developed by Arikawa et al.) that 3-D Navier-Stokes simulation model using the Building Cube Method. In this model, the 3-D Building Cube Method was applied using multiple roots. Moreover, because the calculation area is divided according to simple rules, it has features such as good compatibility with parallel computers, and grid generation by a simple method is useful for calculating the flow around objects with complex shapes. Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/OlJK_Qw_TwY