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Abstract The multiscale, multimesh flow simulators have been designed for the sole purpose of running very large, heterogeneous reservoir, flow problems on massively parallel computers. This paper shows the flow simulation results and the corresponding CPU times. The multiscale flow simulator is written in Fortran 90/95 with OpenMP directives and compiled on high-performance SMP computers. The simulations were performed for several highly heterogeneous, channelized, reservoir cases with realistic rock-fluid interaction (viscous, capillarity, gravity, and compressibility) to evaluate the efficacy of the multiscale, multimesh simulation in parallel computing. It is shown that the aforementioned multiscale technique reduces computing time by several orders of magnitude, while maintaining the same accuracy as the conventional fine scale simulation. Introduction In simulation of displacement processes in large heterogeneous reservoirs, the computing time is both time-consuming and expensive. Therefore there has been a tendency to upscale fine-grid models to reduce the required CPU time. The problem with upscaling is that it often creates inaccuracy in mathematical results (e.g. large numerical dispersion). Upscaling also cannot capture the architecture of the flow channels effectively. Thus, the channeling effects are suppressed. Finally the upscaling algorithm usually does not have a solid physical foundation. For instance, permeability upscaling has been handled through a logical flow averaging technique; however, upscaling of relative permeability curves has not been very well developed. As a consequence, to minimize the upscaling issues, we have resorted to a multimesh, multiscale computing methodology to preserve the reservoir flow and transport characteristics at the very fine-level, while we reduced the inherent computing time by several orders of magnitude. The multiscale computation was reported by several authors previously.7–17 We also presented an extension of the multiscale method for both single- and dual-porosity reservoirs in a previous meeting.5–6 Since then, we have been able to improve our computing methodology, which is the subject of this paper. The multiscale, multimesh simulator was compiled for a 64-bit, SGI-ALTIX with 256 1.5 GHz Itanium2 CPUs. However, for the purpose of this study, we limit our usage with a maximum of 32-CPUs. Computing Methodology We solve the steady-state pressure equation on the global fine-grid mesh, to obtain the flux distribution at the coarse-grid boundaries. These flux distributions are used as the weighting function for the local pressure update instead of the transmissibility weighting used in our previous. We also use the above steady-state fine-grid flux distribution at the boundaries of the coarse-grid to calculate the effective permeability tensor of the coarse-grid. This upscaling approach is different than the classical flow-based permeability upscaling which is based on constant pressure at the boundaries. The latter approach is also equivalent to having a fixed pressure gradient across the coarse-grid domain. The computation sequence:Obtain global fine-scale steady-state pressure solution to calculate the fine-scale flux-weights at the boundaries of each coarse-scale nodes. This information will be used to calculate the fine-scale fluxes within each each coarse-scale nodes. For computational efficiency, for very large grid systems, we use block Jacobi iteration algorithm. For parallel processing purposes, block Jacobi iteration can be done by a red-black ordering scheme. Obtain global unsteady-state coarse-scale pressure solution at large time-step, ?t1, to calculate the coarse-scale fluxes. Calculate the the unsteady-state fine-scale fluxes at the coarse grid boundaries using the coarse-scale fluxes and weighted by Step a. Calculate the fine-scale pressures and internal interface fine-scale boundaries within each coarse-scale gridblocks using the boundary conditions obtained in Step c. Calculate fine-grid saturations using smaller time-steps, ?t2, constrained by the CFL criterion for the IMPES or sequential approach.
Have you noted the change yet? Why is this important to us? In the past, the output of reservoir simulators consisted of reams of fan-fold paper printed with a multitude of numbers and arrays. Eventually, software was developed that could do the job of transforming the numbers to visual displays quickly and easily. This development led to - SPE 84037 increasing dependence on visualization as a way to analyze and report simulation results, Black-Oil Streamline Simulator which arguably led to better simulations.
Tens of millions of cells with complete reservoir parameters now became available for use by the engineer. Althoughupscaling provided a tool to dramatically reduce model sizes, the inherent assumptions of the upscaling techniques left a strong desire by the engineer to incorporate all of the available data in studies. The only available solution to this problem became the subdivision of the model into small segments and the use of parallel computers for reservoir simulation. The introduction of fast, low-cost commodity hardware led to a revolution in higher-performance computing based on clusters.
Introduction Digital numerical simulation has played an increasing role in planning, developing, and depleting oil and gas reservoirs in the past 25 years. Through simulation, alternative development options can be evaluated to optimize development decisions (ref 1). Since all of the North Sea fields have been developed since the advent of numerical simulators, numerous North Sea examples of applying numerical simulation to select reservoir development options exist in the literature. Virtually every field has had some simulation studies leading to its development. The technology advances in both high speed computers and simulation software allow today's petroleum engineer to construct models with an ever petroleum engineer to construct models with an ever increasing degrees of definition and complexity. The proper consideration of the need for a simulation study, the complexity required and the ultimate use all must be considered before a cost effective study can be undertaken. It is all to easy to design a model which is more complex than is required (ref 2). However, the limitations of a simulation model must be considered. A full field model is only as reliable as the data quality which is input. The most efficient use of simulation requires careful consideration of the ultimate use of the model, which frequently is dependent on the stage of field development. Simulation models can be built to represent almost any oil or gas displacement process. Models can simulate laboratory scale results, single well behavior, vertical or areal displacement effects. This discussion will focus primarily on the use of three dimensional, full field simulation to direct the development, management and depletion of oil and associated gas reservoirs. STEPS TO SIMULATION STUDIES It is useful to begin by discussing the steps to building a simulation model. Successfully modelling a field requires that all relevant geologic, production, injection, and surveillance data about production, injection, and surveillance data about a reservoir be analysed prior to building the model. Steps in a simulation study are listed in Table 1. Each of these steps will be discussed briefly below. To fully specify a the black oil simulation input data, each block in the model must contain the information listed in Table 2. This information is collectively known as the reservoir description. Accurately describing the reservoir properties is critical to successful modelling the properties is critical to successful modelling the field. The amount of effort required in this phase varies from field to field, but usually consumes about 20 percent of the total simulation study effort. Two dimensional simulation is employed to investigate the reservoir flow characteristics. Through these investigations, the maximum grid block size and layering can be evaluated. The other main function of the two dimensional models is to develop pseudo relative permeability and pseudo capillary pressure functions. Most full pseudo capillary pressure functions. Most full field simulation models have layer thicknesses too great for laboratory derived relative permeability and capillary pressure relationships to adequately describe fluid flow. The 2-D, fine grid models are usually run, then edited to develop dynamic pseudo relationships which will more accurately represent fluid flow in the model. The last main purpose for the 2-D studies is to develop well functions describing fluid flow characteristics to a wellbore within a grid block as a function of either grid block saturations or fluid contact positions. P. 859
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- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 211/7a > Magnus Field > Kimmeridge Formation > Magnus Formation (0.99)
- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 211/7a > Magnus Field > Kimmeridge Formation > Lower Kimmeridge Clay Formation (0.99)
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