A Novel Approach to Extracting Casing Status Features Using Data Mining

Entropy

Jikai Chen¹  Haoyu Li¹  Yanjun Wang¹  Ronghua Xie² 

[1] Harbin Institute of Technology, School of Electrical Engineering and Automation, Harbin 150001, Heilongjiang, China; E-Mails:;Daqing Oilfield Limited Company, Daqing 163412, Heilongjiang, China; E-Mails:

关键词: casing coupling location signal;    Shannon wavelet time entropy;    Tsallis wavelet singularity entropy;    data mining;    feature extraction;   

DOI  :  10.3390/e16010389

来源: mdpi

【 摘 要 】

Casing coupling location signals provided by the magnetic localizer in retractors are typically used to ascertain the position of casing couplings in horizontal wells. However, the casing coupling location signal is usually submerged in noise, which will result in the failure of casing coupling detection under the harsh logging environment conditions. The limitation of Shannon wavelet time entropy, in the feature extraction of casing status, is presented by analyzing its application mechanism, and a corresponding improved algorithm is subsequently proposed. On the basis of wavelet transform, two derivative algorithms, singular values decomposition and Tsallis entropy theory, are proposed and their physics meanings are researched. Meanwhile, a novel data mining approach to extract casing status features with Tsallis wavelet singularity entropy is put forward in this paper. The theoretical analysis and experiment results indicate that the proposed approach can not only extract the casing coupling features accurately, but also identify the characteristics of perforation and local corrosion in casings. The innovation of the paper is in the use of simple wavelet entropy algorithms to extract the complex nonlinear logging signal features of a horizontal well tractor.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland