DocumentCode :
458867
Title :
Lithology Recognition During Oil Well Drilling Based on Fuzzy-adaptive Hamming Network
Author :
Gao, Tiehong ; Cao, Junyi ; Zhang, Minglu ; Qi, Jiangbo
Author_Institution :
Sch. of Mech. Eng., Hebei Univ. of Technol., Tianjin
Volume :
1
fYear :
2006
fDate :
16-18 Oct. 2006
Firstpage :
574
Lastpage :
578
Abstract :
In order to satisfy the urgent demand for real-time lithology recognition of bit position during oil well drilling, a method of lithology feature extraction had been presented using the relation between curve variation trend of the real-time estimated drillable value and material of core lithology. Based on this method and fuzzy-adaptive Hamming network, a novel method had been proposed to realize real-time lithology recognition. This method avoids the shortages existing in ordinary method based on logging curve and human estimation, such as inaccuracy of lithology, bad real-time ability, etc. The results from simulation experiment of testing samples show that the classifying speed is fast and the classifying is steady and effective. The proposed network is fit to fuzzy quantity and analog quantity with proper quantity of samples and classes
Keywords :
feature extraction; fuzzy set theory; object recognition; oil drilling; core lithology material; fuzzy-adaptive Hamming network; lithology feature extraction; oil well drilling; real-time estimated drillable value; real-time lithology recognition; Data mining; Drilling; Feature extraction; Human factors; Mechanical engineering; Neural networks; Pattern analysis; Pattern recognition; Petroleum; Testing; fuzzy-adaptive Hamming network; lithology recognition; oil well drilling; real-time;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
Type :
conf
DOI :
10.1109/ISDA.2006.186
Filename :
4021502
Link To Document :
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