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Drilling Cost Prediction Based on Self-adaptive Differential Evolution and Support Vector Regression

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Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

Prediction of drilling cost in oil and gas field has an important impact on correct economic decision-making and economic efficiency of oilfield enterprises. Support Vector Machine (SVR) is used to predict the drilling cost of oil and gas in this paper. In order to overcome problem of easy to cause local optimum of SVR, this paper uses self-adaptive differential evolution (SaDE) to train SVR to accelerate the speed of parameters optimization and improve the predictive accuracy of the model. The proposed model is applied to predict the drilling costs in one of the Chinese oil company. SaDE is also compared with three parameter optimization methods, Differential Evolution(DE), Grid Search(GS) and Genetic Algorithms(GA). The experimental results show that, SaDE-SVR model is better than DE-SVR, GS-SVR and GA-SVR in terms of accuracy and convergence speed in predicting the drilling cost. It validates the effectiveness of SaDE-SVR applied in prediction of drilling cost of oil and gas field.

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Author information

Authors and Affiliations

  1. Xingzhi College, Xi’an University of Finace and Economics, Xi’an, 710038, Shaanxi, China

    Huaxian Pan

  2. School of Computer Science, Xi’an Shiyou University, Xi’an, 710065, Shaanxi, China

    Guojian Cheng

  3. Xinjiang Oil Field Company Shixi Field Operational Zone, Karamay, 834000, Xinjiang, China

    Jian Ding

Authors
  1. Huaxian Pan
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  2. Guojian Cheng
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  3. Jian Ding
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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© 2013 Springer-Verlag Berlin Heidelberg

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Pan, H., Cheng, G., Ding, J. (2013). Drilling Cost Prediction Based on Self-adaptive Differential Evolution and Support Vector Regression. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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