Oil reservoir characterization using ensemble data assimilation

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Jafarpour, Behnam

Advisor dc:contributor.advisor

• Dennis McLaughlin.

• Civil and Environmental Engineering.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/43046

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/43046