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The Least Squares SVM for the Prediction of Production in the Field of Oil and Gas

Author

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  • Jun Peng

    (Chongqing University of Science and Technology, Chongqing, China)

  • Yudeng Qiao

    (Chongqing University of Science and Technology, Chongqing, China)

  • Dedong Tang

    (Chongqing University of Science and Technology, Chongqing, China)

  • Lan Ge

    (Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd., Chongqing, China)

  • Qinfeng Xia

    (Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd., Chongqing, China)

  • Tingting Chen

    (Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd., Chongqing, China)

Abstract

With the development of cognitive information technology and continuous application, human society has also accelerated the development. Cognitive information is widely used in the field of oil and gas, where production forecasts are of great importance to firms and companies. In this article, the support vector machine and the least squares support vector machine (LS-SVM) and particle swarm optimization algorithm research, combined to accurately predict and make error estimates. In this article, the model is applied to verify the actual output data of certain enterprises in previous years. The results show that the model has good convergence, high prediction accuracy and training speed, and can predict its output more accurately. The method used in this article is of the development of cognitive information technology, the authors have reason to believe that with the continuous development of cognitive information technology, our society will have a breakthrough.

Suggested Citation

  • Jun Peng & Yudeng Qiao & Dedong Tang & Lan Ge & Qinfeng Xia & Tingting Chen, 2018. "The Least Squares SVM for the Prediction of Production in the Field of Oil and Gas," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(1), pages 60-74, January.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:1:p:60-74
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