Summary
A new parallel, black-oil-production reservoir simulator (Powers**) has been developed and fully integrated into the pre- and post-processing graphical environment. Its primary use is to simulate the giant oil and gas reservoirs of the Middle East using millions of cells. The new simulator has been created for parallelism and scalability, with the aim of making megacell simulation a day-to-day reservoir-management tool. Upon its completion, the parallel simulator was validated against published benchmark problems and other industrial simulators. Several giant oil-reservoir studies have been conducted with million-cell descriptions. This paper presents the model formulation, parallel linear solver, parallel locally refined grids, and parallel well management. The benefits of using megacell simulation models are illustrated by a real field example used to confirm bypassed oil zones and obtain a history match in a short time period. With the new technology, preprocessing, construction, running, and post-processing of megacell models is finally practical. A typical history- match run for a field with 30 to 50 years of production takes only a few hours.
Introduction
With the development of early parallel computers, the attractive speed of these computers got the attention of oil industry researchers. Initial questions were concentrated along these lines:Can one develop a truly parallel reservoir-simulator code?What type of hardware and programming languages should be chosen?
Contrary to seismic, it is well known that reservoir simulator algorithms are not naturally parallel; they are more recursive, and variables display a strong dependency on each other (strong coupling and nonlinearity). This poses a big challenge for the parallelization. On the other hand, if one could develop a parallel code, the speed of computations would increase by at least an order of magnitude; as a result, many large problems could be handled. This capability would also aid our understanding of the fluid flow in a complex reservoir. Additionally, the proper handling of the reservoir heterogeneities should result in more realistic predictions. The other benefit of megacell description is the minimization of upscaling effects and numerical dispersion.
The megacell simulation has a natural application in simulating the world's giant oil and gas reservoirs. For example, a grid size of 50 m or less is used widely for the small and medium-size reservoirs in the world. In contrast, many giant reservoirs in the Middle East use a gridblock size of 250 m or larger; this easily yields a model with more than 1 million cells. Therefore, it is of specific interest to have megacell description and still be able to run fast. Such capability is important for the day-to-day reservoir management of these fields.
This paper is organized as follows: the relevant work in the petroleum-reservoir-simulation literature has been reviewed. This will be followed by the description of the new parallel simulator and the presentation of the numerical solution and parallelism strategies. (The details of the data structures, well handling, and parallel input/output operations are placed in the appendices). The main text also contains a brief description of the parallel linear solver, locally refined grids, and well management. A brief description of megacell pre- and post-processing is presented. Next, we address performance and parallel scalability; this is a key section that demonstrates the degree of parallelization of the simulator. The last section presents four real field simulation examples. These example cases cover all stages of the simulator and provide actual central processing unit (CPU) execution time for each case. As a byproduct, the benefits of megacell simulation are demonstrated by two examples: locating bypassed oil zones, and obtaining a quicker history match. Details of each section can be found in the appendices.
Previous Work
In the 1980s, research on parallel-reservoir simulation had been intensified by the further development of shared-memory and distributed- memory machines. In 1987, Scott et al.1 presented a Multiple Instruction Multiple Data (MIMD) approach to reservoir simulation. Chien2 investigated parallel processing on sharedmemory computers. In early 1990, Li3 presented a parallelized version of a commercial simulator on a shared-memory Cray computer. For the distributed-memory machines, Wheeler4 developed a black-oil simulator on a hypercube in 1989. In the early 1990s, Killough and Bhogeswara5 presented a compositional simulator on an Intel iPSC/860, and Rutledge et al.6 developed an Implicit Pressure Explicit Saturation (IMPES) black-oil reservoir simulator for the CM-2 machine. They showed that reservoir models over 2 million cells could be run on this type of machine with 65,536 processors. This paper stated that computational speeds in the order of 1 gigaflop in the matrix construction and solution were achievable.
In mid-1995, more investigators published reservoir-simulation papers that focused on distributed-memory machines. Kaarstad7 presented a 2D oil/water research simulator running on a 16384 processor MasPar MP-2 machine. He showed that a model problem using 1 million gridpoints could be solved in a few minutes of computer time. Rame and Delshad8 parallelized a chemical flooding code (UTCHEM) and tested it on a variety of systems for scalability. This paper also included test results on Intel iPSC/960, CM-5, Kendall Square, and Cray T3D.
PGHPF – An Optimizing High Performance Fortran Compiler for Distributed Memory Machines