Design and development of an artificial neural network for estimation of formation permeability
- West Virginia Univ., Morgantown, WV (United States)
Permeability is one of the most important characteristics of hydrocarbon-bearing formations and one of the most important pieces of information in the design and management of enhanced recovery operations. With accurate knowledge of permeability, petroleum engineers can manage the production process of a field efficiently. Although formation permeability is often measured in the laboratory from cores or evaluated from well-test data, core analysis and well-test data are only available from a few wells in a field, while the majority of wells are logged. In this study, the authors have designed an artificial neural network that can accurately predict the permeability of the formations by use of the data provided by geophysical well logs. Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 170109
- Journal Information:
- SPE Computer Applications, Journal Name: SPE Computer Applications Journal Issue: 6 Vol. 7; ISSN 1064-9778; ISSN SCAPEP
- Country of Publication:
- United States
- Language:
- English
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