A Fully Implicit Parallel EOS Compositional Simulator for Large Scale Reservoir Simulation

Wang, P.; Balay, Satish; Sepehrnoori, Kamy; Wheeler, John Archibald; Abate, Jason; Barry, Smith; Pope, Gary A.

doi:10.2118/51885-ms

Cited by 44 publications

(11 citation statements)

References 10 publications

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“…For reservoir simulation, work has primarily been done for distributed architectures and distributed memory models and is discussed, for example, in the works by Tchelepi et al [4], Wheeler et al [30], Elmroth et al [31], Zhangxin et al [17], and Abate et al [1]. To our knowledge, no prior works exist for shared memory models in reservoir simulation.…”

Section: Parallel Methods and Parallelization In Reservoir Simulationmentioning

confidence: 99%

Parallel implementations of streamline simulators

2008

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We discuss various strategies for parallelizing streamline simulators and present a single-phase shared memory implementation. The choice of a shared memory programming model is motivated by its suitability for streamline simulation, as well as the rapid advance of multicore processors, which are readily available at low-cost. We show that streamline-based methods are easily parallelizable on shared memory architectures through their decomposition of the multidimensional transport equations into a large set of independent 1D transport solves. We tested both a specialized explicit load balancing algorithm that optimizes the streamline load distribution across threads to minimize the time that any of the threads are idle, and the dynamic load balancing algorithms provided by OpenMP on the shared memory machines. Our results clearly indicate that built-in schedulers are competitive with specialized load balancing strategies as long as the number of streamlines per thread is sufficiently high, which is the case in field applications. The average workload per thread is nominally insensitive to workload variations between individual streamlines, and any load balancing advantage offered by explicit strategies is not sufficient to overcome associated computational and parallel overhead. In terms of the allocation of streamlines or streamline segments to threads, we investigated both the distributed approach, in which threads are assigned streamline segments, and the owner approach, in which threads own complete streamlines. We found that the owner approach is most suitable. The slight advantage that the distributed approach has in terms of load balancing is not enough to compensate for the additional overheads. Moreover, the owner approach allows straightforward re-use of existing sequential codes, which is not the case for the distributed approach in case of implicit or adaptive implicit solution strategies. The tracing and mapping stages in streamline simulation have low parallel efficiency. However, in real-field models, the computational burden of the streamline solves is significantly heavier than that of the tracing and mapping stages, and therefore, the impact of these stages is limited. We tested the parallelization on three shared memory systems: a 24 dualcore processor Sun SPARC server; an eight-way Sun Opteron server, representative of the state-of-the-art shared memory systems in use in the industry; and the very recently released Sun Niagara II multicore machine that has eight floating point compute units on the chip. We test a single-phase flow problem on three heterogeneous reservoirs with varying well placements (this system gives the worst case scenario as the tracing and mapping costs are not negligible compared to the transport costs). For the SPARC and Opteron system, we find parallel efficiencies ranging between 60 and 75 for the tracer flow problems. The sublinear speedup is mostly due to communication overheads in the tracing and mapping stages. In applications with more complex physics, the relati...

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Section: Parallel Methods and Parallelization In Reservoir Simulationmentioning

confidence: 99%

Parallel implementations of streamline simulators

2008

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“…We used a compositional reservoir simulator called GPAS (Wang et al, 1997(Wang et al, , 1999Han et al, 2005) in our coupled wellbore/reservoir model. A brief description of the simulator is presented below.…”

Section: Reservoir Governing Equationsmentioning

confidence: 99%

“…Different cases are modeled with this coupled simulator to show when the compositional approach is important and when the blackoil approximation is accurate enough to predict pressure and temperature profiles in the wellbore. For a reservoir simulator, we used a compositional simulator called GPAS (Wang et al, 1997(Wang et al, , 1999Han et al, 2005) which was developed at the University of Texas at Austin.…”

Section: Introductionmentioning

confidence: 99%

A compositional wellbore/reservoir simulator to model multiphase flow and temperature distribution

Pourafshary

Varavei

Sepehrnoori

et al. 2009

Journal of Petroleum Science and Engineering

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“…In the present study we consider PETSc as a black box hence it will only briefly be discussed. Interested readers are referred to the PETSc user manual or publications on application simulations developed by PETSc users [39][40][41][42].…”

Section: Continuous Phasementioning

confidence: 99%

“…The number of subdomain blocks for the block Jacobi is set to one (default value) so that each processor gets a complete subdomain of the problem and does a single local incomplete factorization on the Jacobian corresponding to this subdomain [42]. To increase convergence rate, elements in the off diagonal block matrix which are responsible for the coupling terms between processors should be minimized.…”

Section: Continuous Phasementioning

confidence: 99%