An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

Jun Ni; Zhan Li Ren; Guo Qing Han

doi:10.4028/www.scientific.net/AMR.524-527.1331

Paper Titles

A Novel Approach of Cuttings Transport with Bubbles in Horizontal Wells
p.1314

A Study of a New Stage Cementing Tool and its Field Application
p.1318

Air Foam Injection for EOR in Light Oil Reservoirs with High Heterogeneity
p.1322

An Application of BP Neural Network Model to Predict the Moisture Content of Crude Oil
p.1327

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card
p.1331

Analysis of Acoustic Propagation Characteristic of Near-Bit Drill Tools
p.1335

Analysis of Interwell Communication by Rate Correlation Degree Method
p.1339

Analysis of Perforation Parameters under Fluid-Solid Coupling Theory
p.1344

Application of Dynamic Data Mining and Case-Based Reasoning in the Drilling Fault Diagnosis
p.1350

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 524-527An Improved Artificial Neural Network for...

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

Abstract:

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

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Periodical:

Advanced Materials Research (Volumes 524-527)

Pages:

1331-1334

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.524-527.1331

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Online since:

May 2012

Authors:

Jun Ni, Zhan Li Ren, Guo Qing Han

Keywords:

Artificial Neural Network (ANN), Dynamometer Card, Pattern Recognition

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