Neural network applications to reservoirs: Physics-based models and data models-Reference-Cited by-同舟云学术

Neural network applications to reservoirs: Physics-based models and data models

Published:2014-11 Issue: Volume:123 Page:1-6
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Aïfa Tahar

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference28 articles.

1. Artificial Intelligence Techniques in Reservoir Characterization: Functional Networks Softsensor for Formation Porosity and Water Saturation in Oil Wells;Adeniran,2010

2. Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: application of particles warm optimization;Ahmadi;J. Pet. Sci. Eng.,2014

3. Connectionist model predicts porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence;Ahmadi;J. Pet. Sci. Eng.,2014

4. New tools to determine bubble point pressure of crude oils: experimental and modeling study;Ahmadi;J. Pet. Sci. Eng.,2014

5. Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oilfield;Aïfa;Algeria. J. Pet. Sci. Eng.,2014

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