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Identification method of oil-bearing reservoirs based on gas logging data
LI Han-lin1,LIAN Cheng-bo1,MA Shi-kun1,LIU Ming-yan2
(1.Faculty of GeoResource and Information in China University of Petroleum,Dongying 257061, Siandong Province, China;2. Shengli Petroleum Administration,Dongying 257055,Shandong Province , China )
Abstract:
The methods of fuzzy model identification and error back-propagation(BP) artificial neural network(ANN) were used to analyze oil-bearing reservoirs based on gas logging information. The results show that there is a noticeable correlativ-ity between gas legging data and oil-bearing reservoirs. The distinction of the principles results in the difference of the identifying results that the former is fuzzy and the latter is random. The fuzzy cx>rrelativity between gas logging data and oil-bearing reservoirs can be determined and the random property of gas legging data can be adjusted by fuzzy model. The method of error BP ANN can indicate the nc?i-Hnear correlation between gas legging data and oil-bearing reservoirs accurately and be hard to adjust the randcan property of gas logging data. The methods used to identify oil-bearing reservoirs are feasible. The identifying results show that the former method is better than the latter method.
Key words:  gas loggings back-propagation artificial neural network  fuzzy model identification  oil-bearing reservoir