Title page for ETD etd-11112009-174803

Type of Document Master's Thesis
Author El-Khamra, Yaakoub Youssef
Author's Email Address yelkhamra@gmail.com
URN etd-11112009-174803
Title Real-Time Reservoir Characterization and Beyond: CyberInfrastructure Tools and Technologies
Degree Master of Science in Petroleum Engineering (M.S.P.E.)
Department Petroleum Engineering
Advisory Committee
Advisor Name Title
White, Christopher Committee Chair
Bourdin, Blaise Committee Member
Hughes, Richard Committee Member
Jha, Shantenu Committee Member
Tyagi, Mayank Committee Member
  • Reservoir Simulation
  • Ensemble Kalman filter
  • EnKF
  • Closed Loop
Date of Defense 2009-11-06
Availability unrestricted
The advent of the digital oil eld and rapidly decreasing cost of computing creates opportunities

as well as challenges in simulation based reservoir studies, in particular, real-time

reservoir characterization and optimization. One challenge our e orts are directed toward

is the use of real-time production data to perform live reservoir characterization

using high throughput, high performance computing environments. To that end we developed

the required tools of parallel reservoir simulator, parallel ensemble Kalman lter

and a scalable work

ow manager.

When using this collection of tools, a reservoir modeler is able to perform large

scale reservoir management studies in short periods of time. This includes studies with

thousands of models that are individually complex and large, involving millions of degrees

of freedom. Using parallel processing, we are able to solve these models much faster than

we otherwise would on a single, serial machine. This motivated the development of a fast

parallel reservoir simulator. Furthermore, distributing those simulations across resources

leads to a smaller total time to completion by making use of distributed processing.

This allows the development of a scalable high throughput work

ow manager. Finally,

with thousands of models, each with millions of degrees of freedom, we end up with a


uity of model parameters. This translates directly to billions of degrees of freedom

in the reservoir study. To be able to use the ensemble Kalman lter on these models, we

needed to develop a parallel implementation of the ensemble Kalman lter.

This thesis discusses the enabling tools and technologies developed to address a speci

c problem: how to accurately characterize reservoirs, using large numbers of complex

detailed models. For these characterization studies to be helpful in making production

decisions, the time to solution must be feasible. To that end, our work is focused on

developing and extending these tools, and optimizing their performance.

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