Pore Pressure Prediction and Modeling Using Well-Logging Data in Bai Hassan Oil Field Northern Iraq | OMICS International | Abstract
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
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Research Article

Pore Pressure Prediction and Modeling Using Well-Logging Data in Bai Hassan Oil Field Northern Iraq

FQays MS* and Wan IWY
Department of Petroleum Geoscience, Universiti Teknologi Petronas, Malaysia
Corresponding Author : Qays Mohammed Sadeq
Department of Petroleum Geoscience
Universiti Teknologi Petronas, Malaysia
Tel: +60-5-368 7100/7040
E-mail: [email protected]
Received June 10, 2015; Accepted August 13, 2015; Published August 23, 2015
Citation: Qays MS, Wan IWY (2015) Pore Pressure Prediction and Modeling Using Well-Logging Data in Bai Hassan Oil Field Northern Iraq. J Earth Sci Clim Change 6: 290. doi:10.4172/2157-7617.1000290
Copyright: © 2015 Qays MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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The Bai Hassan Field is one of the Iraq’s giant oil fields with multiple pay zones similar to most of the northern Iraq oil fields. Knowledge of pore pressure is essential for economically and save well planning and efficient reservoir modeling. Pore pressure prediction has an important application in proper selection of the casing points and a reliable mud weight. In addition, using cost-effective methods of pore pressure prediction, which give extensive and continuous range of data, is much reasonable than direct measuring of pore pressure. The main objective of this project is to determine the pore pressure using well log data in Bai Hassan oil fields. To obtain this goal, the formation pore pressure is predicted from well logging data by applying three different methods including the Eaton, the Bowers and the compressibility methods. Predicted results have to show that the best correlation with the measured pressure data must achieved by the modified Eaton method with Eaton's exponent of about 0.5. Finally, in order to generate the 3D pore pressure model, well-log-based estimated pore pressures from the Eaton method will upscale and distribute throughout the 3D structural grid using a geo statistical approach. The 3D pore pressure model has to show good agreement with the well-log-based estimated pore pressure and the measured pressure obtained from modular formation dynamics tester.


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