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Oil reservoir characterization using ensemble data assimilation

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dc.contributor.advisor Dennis McLaughlin. en_US
dc.contributor.author Jafarpour, Behnam en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.date.accessioned 2008-11-07T18:55:52Z
dc.date.available 2008-11-07T18:55:52Z
dc.date.copyright 2008 en_US
dc.date.issued 2008 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/43046
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2008. en_US
dc.description Pages 211-212 blank. en_US
dc.description Includes bibliographical references. en_US
dc.description.abstract Increasing world energy demand combined with decreasing discoveries of new and accessible hydrocarbon reserves are necessitating optimal recovery from the world's current hydrocarbon resources. Advances in drilling and monitoring technologies have introduced intelligent oilfields that provide real-time measurements of reservoir conditions. These measurements can be used for more frequent reservoir model calibration and characterization that can lead to improved oil recovery though model-based closed-loop control and management. This thesis proposes an efficient method for probabilistic characterization of reservoir states and properties. The proposed algorithm uses an ensemble data assimilation approach to provide stochastic characterization of reservoir attributes by conditioning individual prior ensemble members on dynamic production observations at wells. The conditioning is based on the second-order Kalman filter analysis and is performed recursively, which is suitable for real-time control applications. The prior sample mean and covariance are derived from nonlinear dynamic propagation of an initial ensemble of reservoir properties. Realistic generation of these initial reservoir properties is shown to be critical for successful performance of the filter. When properly designed and implemented, recursive ensemble filtering is concluded to be a practical and attractive alternative to classical iterative history matching algorithms. A reduced representation of reservoir's states and parameters using discrete cosine transform is presented to improve the estimation problem and geological consistency of the results. The discrete cosine transform allows for efficient, flexible, and robust parameterization of reservoir properties and can be used to eliminate redundancy in reservoir description while preserving important geological features. en_US
dc.description.abstract This improves under-constrained inverse problems such as reservoir history matching in which the number of unknowns significantly exceeds available data. The proposed parameterization approach is general and can be applied with any inversion algorithm. The suitability of the proposed estimation framework for hydrocarbon reservoir characterization is demonstrated through several water flooding examples using synthetic reservoir models. en_US
dc.description.statementofresponsibility by Behnam Jafarpour. en_US
dc.format.extent 212 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Civil and Environmental Engineering. en_US
dc.title Oil reservoir characterization using ensemble data assimilation en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.identifier.oclc 243783313 en_US


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