Enabling interactive and collaborative oil reservoir simulations on the Grid

Parashar, Manish; Muralidhar, Rajeev; Lee, Wonsuck; Arnold, Dorian; Dongarra, Jack; Wheeler, Mary F.

doi:10.1002/cpe.899

Cited by 11 publications

(10 citation statements)

References 19 publications

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“…The results demonstrate novel approaches to solutions to the underlying computing and data challenges. Efficient parallel methods for seismic imaging and reservoir simulation are discussed in [1][2][3]. Scalable Grid-oriented solutions to data intense problems arising in the reservoir characterization-simulation loop are proposed and evaluated in [4].…”

Section: Introductionmentioning

confidence: 99%

“…Scalable Grid-oriented solutions to data intense problems arising in the reservoir characterization-simulation loop are proposed and evaluated in [4]. Problems arising in Grid-based simulations of coupled reservoir models are addressed in [2,4]. In addition, the collaborative and interactive steering capabilities are proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Problems arising in Grid-based simulations of coupled reservoir models are addressed in [2,4]. In addition, the collaborative and interactive steering capabilities are proposed in [2]. Finally, different approaches to software framework …”

Section: Introductionmentioning

confidence: 99%

“…In addition, the collaborative and interactive steering capabilities are proposed in [2]. Finally, different approaches to software framework …”

mentioning

confidence: 99%

“…EDITORIAL construction are discussed in [2,3]; the common theme is that the underlying framework methodology must be abstract enough to include a variety of applications yet it must keep the overhead to a reasonable minimum in order to offer a competitive edge to a particular application.…”

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Special Issue: High‐Performance Computing in Geosciences

Peszyńska

Wheeler

2005

Concurrency and Computation

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INTRODUCTIONThis special issue is dedicated to several aspects of state-of-the-art computing for geoscience applications with a focus on subsurface phenomena.The underlying comprehensive numerical models are the basis for predictive simulations of remediation scenarios and recovery processes in subsurface reservoirs. Such models are typically based on continuous Galerkin finite-element methods or cell-centered finite differences and more recently on mixed finite element, finite volume, or mimetic finite differences.The data for such simulations is typically sparse, and is frequently considered non-deterministic. The associated uncertainty of simulation results can be decreased thanks to the improved classic and to the new emerging reservoir characterization tools, which include seismic imaging and sensor technologies. Such methods deliver enormous volumes of data which have to be assimilated with the simulation results. The resulting reservoir characterization-simulation loop presents enormous computational and data challenges.The computational challenges arise from multiscale character and complexity of the numerical models, which require sophisticated nonlinear and linear solver techniques. In spite of recent dramatic increases in computational power there is a constant need to improve existing simulation techniques implemented on state-of-the-art large-scale parallel platforms as well as on commodity clusters. In addition, scalable Grid-based technologies are necessary in order to fully exploit the existing distributed computational and storage resources. Several disciplines still lack the appropriate tools to handle large-scale simulation projects and it is rare to have tools that span multiple and interdisciplinary projects.This special issue is a collection of papers on the above-mentioned aspects of parallel and distributed computing for geoscience applications and includes both the tools as well as the applications perspective. The results demonstrate novel approaches to solutions to the underlying computing and data challenges. Efficient parallel methods for seismic imaging and reservoir simulation are discussed in [1][2][3]. Scalable Grid-oriented solutions to data intense problems arising in the reservoir characterization-simulation loop are proposed and evaluated in [4]. Problems arising in Grid-based simulations of coupled reservoir models are addressed in [2,4]. In addition, the collaborative and interactive steering capabilities are proposed in [2]. Finally, different approaches to software framework 1364EDITORIAL construction are discussed in [2,3]; the common theme is that the underlying framework methodology must be abstract enough to include a variety of applications yet it must keep the overhead to a reasonable minimum in order to offer a competitive edge to a particular application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In addition, the collaborative and interactive steering capabilities are proposed in [2]. Finally, different approaches to software framework …”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Special Issue: High‐Performance Computing in Geosciences

Peszyńska

Wheeler

2005

Concurrency and Computation

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A high‐performance computing framework for Monte Carlo ocean color simulations

Kajiyama

D’Alimonte

Cunha

2016

Concurrency and Computation

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Summary This paper presents a high‐performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in‐water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run‐time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large‐scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC‐oriented problem‐solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons, Ltd.

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Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

Parashar

Matossian

Klíe

et al. 2006

Computational Science – ICCS 2006

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Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multiphysics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Gridbased adaptive execution engine, distributed data management services for real-time data access, exploration, and coupling, and self-managing middleware services for seamless discovery and composition of components, services, and data on the Grid. This paper investigates how these solutions can be leveraged and applied to address another DDDAS application of strategic importance -the data-driven management of Ruby Gulch Waste Repository.

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Enabling interactive and collaborative oil reservoir simulations on the Grid

Cited by 11 publications

References 19 publications

Special Issue: High‐Performance Computing in Geosciences

Special Issue: High‐Performance Computing in Geosciences

A high‐performance computing framework for Monte Carlo ocean color simulations

Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

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