Reservoir Model Updating by Ensemble Kalman Filter – Practical Approaches Using Grid Computing Technology

Li, X.; Zhou, Lei; White, Chris D.; Allen, Gabrielle

doi:10.3997/2214-4609.201403105

Cited by 9 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, for a small ensemble size, the spread among the ensemble members decreases too rapidly after each update. This yields the ensembles with a very small covariance and finally causes the EnKS divergence [ Li , 2008]. On the other hand, by increasing the ensemble size the computational cost increases.…”

Section: Resultsmentioning

confidence: 99%

Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

Bateni

Entekhabi

2012

Water Resources Research

View full text Add to dashboard Cite

1] The estimation of surface heat fluxes based on the assimilation of land surface temperature (LST) has been achieved within a variational data assimilation (VDA) framework. Variational approaches require the development of an adjoint model, which is difficult to derive and code in the presence of thresholds and discontinuities. Also, it is computationally expensive to obtain the background error covariance for the variational approaches. Moreover, the variational schemes cannot directly provide statistical information on the accuracy of their estimates. To overcome these shortcomings, we develop an alternative data assimilation (DA) procedure based on ensemble Kalman smoother (EnKS) with the state augmentation method. The unknowns of the assimilation scheme are neutral turbulent heat transfer coefficient (that scales the sum of turbulent heat fluxes) and evaporative fraction, EF (that represents partitioning among the turbulent fluxes). The new methodology is illustrated with an application to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings and flux observations. The results indicate that the EnKS model not only provides reasonably accurate estimates of EF and turbulent heat fluxes but also enables us to determine the uncertainty of estimations under various land surface hydrological conditions. The results of the EnKS model are also compared with those of an optimal smoother (a dynamic variational model). It is found that the EnKS model estimates are less than optimal. However, the degree of suboptimality is small, and its outcomes are roughly comparable to those of an optimal smoother. Overall, the results from this test indicate that EnKS is an efficient and flexible data assimilation procedure that is able to extract useful information on the partitioning of available surface energy from LST measurements and eventually provides reliable estimates of turbulent heat fluxes.Citation: Bateni, S. M., and D. Entekhabi (2012), Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters, Water Resour. Res., 48, W08521,

show abstract

Section: Resultsmentioning

confidence: 99%

Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

Bateni

Entekhabi

2012

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…EnKF represent a promising approach to history matching [21], [22], [23], [24]. EnKF are recursive filters that can be used to handle large, noisy data; the data in this case are the results and parameters from an ensemble of reservoir models that are sent through the filter to obtain the "true state" of the data.…”

Section: Enkfbased History Matchingmentioning

confidence: 99%

Exploring application and infrastructure adaptation on hybrid grid-cloud infrastructure

Kim

El-Khamra

Jha

et al. 2010

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

View full text Add to dashboard Cite

Clouds are emerging as an important class of distributed computational resources and are quickly becoming an integral part of production computational infrastructures. An important but oft-neglected question is, what new applications and application capabilities can be supported by clouds as part of a hybrid computational platform? In this paper we use the ensemble Kalman-filter based dynamic application workflow and investigate how clouds can be effectively used as an accelerator to address changing computational requirements as well as changing Quality of Service constraints (e.g., deadlines). Furthermore, we explore how application and system-level adaptivity can be used to improve application performance and achieve a more effective utilization of the hybrid platform. Specifically, we adapt the ensemble Kalman-filter based application formulation (serial versus parallel, different solvers etc.) so as to execute efficiently on a range of different infrastructure (from High Performance Computing grids to clouds that support single core and many-core virtual machines). Our results show that there are performance advantages to be had by supporting application and infrastructure level adaptivity. In general, we find that grid-cloud infrastructure can support novel usage modes, such as deadline-driven scheduling, for applications with tunable characteristics that can adapt to varying resource types.

show abstract

“…Among history matching techniques, the automatic history matching through Ensemble Kalman filters (EnKF) technique represents a promising approach that has gained a lot of popularity recently [15,16,18,22]. In a typical EnKF study, an ensemble of models (of varying size and duration) is run through a reservoir simulator and their results are analyzed.…”

Section: Application Descriptionmentioning

confidence: 99%

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Kim

El-Khamra

Rodero

et al. 2011

Scientific Programming

View full text Add to dashboard Cite

In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints. The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.

show abstract

Reservoir Model Updating by Ensemble Kalman Filter – Practical Approaches Using Grid Computing Technology

Cited by 9 publications

References 0 publications

Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

Exploring application and infrastructure adaptation on hybrid grid-cloud infrastructure

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Contact Info

Product

Resources

About