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Research on an intelligent drilling parameter optimization method using sliding window segmentation based on the

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This study introduces an intelligent approach to optimize oil and gas drilling parameters using a hydraulic-mechanical specific energy (MSE) model. The method enhances drilling efficiency by 43.34% through advanced data fusion and optimization techniques.

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Area of Science:

  • Petroleum Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Optimizing drilling parameters is crucial for enhancing efficiency and reducing costs in oil and gas operations.
  • Current methods often lack the sophistication to fully integrate complex hydraulic and mechanical data for real-time adjustments.

Purpose of the Study:

  • To develop an intelligent optimization approach for drilling parameters using a hydraulic-mechanical specific energy (MSE) model.
  • To improve drilling efficiency and reduce operational costs in oil and gas engineering.

Main Methods:

  • A time-series data fusion framework was established, integrating Savitzky-Golay filtering, random forest, and hybrid anomaly detection.
  • The MSE model was refined by coupling the rate of penetration (ROP) equation with a backpropagation (BP) neural network.
  • The simulated annealing algorithm was used for global optimization of key drilling parameters.

Main Results:

  • The integrated model achieved prediction accuracies of 70% (ROP equation) and 90% (BP neural network).
  • Field validation identified weight on bit, rotary speed, and flow rate as dominant factors influencing mechanical specific energy.
  • The optimization process resulted in an average improvement of 43.34% in drilling efficiency.

Conclusions:

  • The proposed intelligent optimization method significantly enhances time-series data processing and prediction accuracy.
  • The multi-objective optimization model demonstrates superior adaptability for intelligent drilling parameter optimization under field conditions.
  • This approach offers a practical and effective tool for the oil and gas industry.