Application on Lithology Recognition with BP Artificial Neural Network

Author

Zhou, Jinhui ; Yan, Jienian ; Pan, Li

Author_Institution

Coll. of Pet. Eng., China Univ. of Pet., Beijing, China

Volume

1

fYear

2009

fDate

21-22 Nov. 2009

Firstpage

56

Lastpage

59

Abstract

An artificial neural network (ANN) model is established to recognize the drilled formations´ lithologies while drilling. The styles of output and input of ANN are designed. The nerve cells in input layer are weight of bit (WOB), speed of rotary (SOR) and rate of penetration (ROP). The number of nerve cells in output layer is designed to be three. Software system for recognizing the formation lithologies is developed basing on the error back-propagation (BP) network. The drilling data with microbit drilling and field drilling of petroleum and coal are used to validate the software system. The results indicate that the effect of recognition of formation lithology is better. The average correct ratios achieve 80%, 78% and 95% respectively in the test of microbit drilling, well H12-9 and well 107.

Keywords

backpropagation; coal; drilling (geotechnical); mining industry; petroleum industry; artificial neural network; backpropagation network; coal; drilled formation lithologies; field drilling; lithology recognition; microbit drilling; petroleum; software system; well 107; well H12-9; Artificial intelligence; Artificial neural networks; Drilling; Educational institutions; Information technology; Intelligent networks; Neural networks; Petroleum; Software systems; Testing; Artificial Neural Network; Drilling; Formation Lithology; Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on

Conference_Location

Nanchang

Print_ISBN

978-0-7695-3859-4

Type

conf

DOI

10.1109/IITA.2009.156

Filename

5368608