Type of Document Master's Thesis Author El-Khamra, Yaakoub Youssef Author's Email Address firstname.lastname@example.org 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 Keywords
- Reservoir Simulation
- Ensemble Kalman filter
- Closed Loop
Date of Defense 2009-11-06 Availability unrestricted AbstractThe 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 eorts 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
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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