NDLI: Soft computing for reservoir characterization and management

Content Provider	IEEE Xplore Digital Library
Author	Nikravesh, M.
Copyright Year	2005
Description	Author affiliation: Dept. of EECS, California Univ., Berkeley, CA, USA (Nikravesh, M.)
Abstract	Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	593
Ending Page	598
File Size	3472759
Page Count	6
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Data analysis Data engineering Reservoir characterization Pattern recognition Risk analysis Data mining Well logging Petroleum Neural Network Feature extraction Soft Computing Risk management Hydrocarbon reservoirs Fuzzy Logic
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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