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
Keywords	Reservoir Simulation Ensemble Kalman filter EnKF Closed Loop
Date of Defense	2009-11-06
Availability	unrestricted
Abstract 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 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 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 super 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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