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Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method

Author

Listed:
  • Jinbo Song

    (School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
    Key Laboratory of Drilling and Production Engineering for Oil and Gas, Wuhan 430100, China)

  • Jianlong Wang

    (Engineering and Technology Research Institute of CNPC Bohai Drilling Engineering Co., Ltd., Tianjin 300457, China)

  • Bingqing Li

    (CNPC Xibu Drilling Engineering Co., Ltd., Urumqi 830011, China)

  • Linlin Gan

    (Engineering and Technology Research Institute of CNPC Bohai Drilling Engineering Co., Ltd., Tianjin 300457, China)

  • Feifei Zhang

    (School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
    Key Laboratory of Drilling and Production Engineering for Oil and Gas, Wuhan 430100, China)

  • Xueying Wang

    (School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
    Key Laboratory of Drilling and Production Engineering for Oil and Gas, Wuhan 430100, China)

  • Qiong Wu

    (PetroChina Tarim Oilfield Branch, Korla 841000, China)

Abstract

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs; the mechanical specific energy and unit footage cost as multi-objective optimization functions; and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency.

Suggested Citation

  • Jinbo Song & Jianlong Wang & Bingqing Li & Linlin Gan & Feifei Zhang & Xueying Wang & Qiong Wu, 2022. "Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method," Energies, MDPI, vol. 15(21), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8030-:d:956485
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