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Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model

Author

Listed:
  • Hu Yin

    (School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Menghan Si

    (School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Qian Li

    (School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Jinke Zhang

    (School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Liming Dai

    (Industrial Systems Engineering, University of Regina, Regina, SK S4S0A2, Canada)

Abstract

Timely forecasting of the kick risk after a well kick can reduce the waiting time after well shut-in and provide more time for well killing operations. At present, the multiphase flow model is used to simulate and forecast the pit gain and casing pressure. Due to the complexity of downhole conditions, calculation of the multiphase flow model is difficult. In this paper, the time series analysis method is used to excavate the information contained in the time-varying data of pit gain and casing pressure. A forecasting model based on a time series analysis method of pit gain and casing pressure is established to forecast the pit gain and casing pressure after a kick. To divide the kick risk level and achieve the forecasting of the kick risk before and after well shut-in, kick risk analysis plates based on pit gain and casing pressure are established. Three pit gain cases and one casing pressure case are studied, and a comparison between measured data and predicted data shows that the proposed method has high prediction accuracy and repeatability.

Suggested Citation

  • Hu Yin & Menghan Si & Qian Li & Jinke Zhang & Liming Dai, 2019. "Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model," Energies, MDPI, vol. 12(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3540-:d:267549
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    References listed on IDEAS

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    1. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    2. C. Agiakloglou & P. Newbold, 1992. "Empirical Evidence On Dickey‐Fuller‐Type Tests," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(6), pages 471-483, November.
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    Cited by:

    1. Haifeng Zhu & Ming Xiang & Zhiqiang Lin & Jicheng Yang & Xuerui Wang & Xueqi Liu & Zhiyuan Wang, 2024. "Study on the Mechanism of Gas Intrusion and Its Transportation in a Wellbore under Shut-in Conditions," Energies, MDPI, vol. 17(1), pages 1-17, January.

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