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Peszynska, Malgorzata; Wheeler, Mary F.
doi: 10.1002/cpe.897pmid: N/A
The aim of seismic waveform inversion is to estimate the elastic properties of the Earth's subsurface layers from recordings of seismic waveform data. This is usually accomplished by using constrained optimization often based on very simplistic assumptions. Full waveform inversion uses a more accurate wave propagation model but is extremely difficult to use for routine analysis and interpretation. This is because computational difficulties arise due to: (1) strong nonlinearity of the inverse problem; (2) extreme ill‐posedness; and (3) large dimensions of data and model spaces. We show that some of these difficulties can be overcome by using: (1) an improved forward problem solver and efficient technique to generate sensitivity matrix; (2) an iteration adaptive regularized truncated Gauss–Newton technique; (3) an efficient technique for matrix–matrix and matrix–vector multiplication; and (4) a parallel programming implementation with a distributed system of processors. We use a message‐passing interface in the parallel programming environment. We present inversion results for synthetic and field data, and a performance analysis of our parallel implementation. Copyright © 2005 John Wiley & Sons, Ltd.
Peszynska, Malgorzata; Wheeler, Mary F.
doi: 10.1002/cpe.899pmid: N/A
Grid‐enabled infrastructures and problem‐solving environments can significantly increase the scale, cost‐effectiveness and utility of scientific simulations, enabling highly accurate simulations that provide in‐depth insight into complex phenomena. This paper presents a prototype of such an environment, i.e. an interactive and collaborative problem‐solving environment for the formulation, development, deployment and management of oil reservoir and environmental flow simulations in computational Grid environments. The project builds on three independent research efforts: (1) the IPARS oil reservoir and environmental flow simulation framework; (2) the NetSolve Grid engine; and (3) the Discover Grid‐based computational collaboratory. Its primary objective is to demonstrate the advantages of an integrated simulation infrastructure towards effectively supporting scientific investigation on the Grid, and to investigate the components and capabilities of such an infrastructure. Copyright © 2005 John Wiley & Sons, Ltd.
Peszynska, Malgorzata; Wheeler, Mary F.
doi: 10.1002/cpe.900pmid: N/A
Advanced parallel applications based on the message‐passing paradigm are difficult to design and implement, especially when solution adaptive techniques are used and three‐dimensional problems on complex geometries are faced, which yield the use of unstructured Grids. We present the building blocks for a parallel‐adaptive scheme for the solution of time‐dependent and nonlinear partial differential equations. To minimize computational requirements, h‐adaptivity is introduced via parallel, local Grid adaptation. Novel techniques to avoid hanging nodes are introduced, these assure conforming meshes of hybrid element type in three space dimensions. As a core of the adaptive scheme, local multigrid methods are used to solve the arising linear systems rapidly in parallel. Dynamic Grid changes from h‐adaptivity lead to load imbalance during run time, therefore dynamic load balancing and migration is performed to exploit the aggregated performance of large processor sets efficiently. Real‐world calculations arising from density‐driven flow problems in porous media are performed using the presented parallel‐adaptive solution strategy. The computations are analyzed with regard to speedup. Timings of Grid adaptation, dynamic load balancing/migration and numerical solution scheme show that large‐scale runs on 512 processors gain an overall parallel, numerical speedup of up to 278. A further reduction of the element count by h‐adaptivity by a factor of up to 195 shows the enormous capabilities of the presented parallel‐adaptive multigrid based solution scheme. Copyright © 2005 John Wiley & Sons, Ltd.
Peszynska, Malgorzata; Wheeler, Mary F.
doi: 10.1002/cpe.898pmid: N/A
The main goal of oil reservoir management is to provide more efficient, cost‐effective and environmentally safer production of oil from reservoirs. Numerical simulations can aid in the design and implementation of optimal production strategies. However, traditional simulation‐based approaches to optimizing reservoir management are rapidly overwhelmed by data volume when large numbers of realizations are sought using detailed geologic descriptions. In this paper, we describe a software architecture to facilitate large‐scale simulation studies, involving ensembles of long‐running simulations and analysis of vast volumes of output data. Copyright © 2005 John Wiley & Sons, Ltd.
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