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This study uses machine learning, specifically artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs), to accurately predict Equivalent Circulating Density (ECD) using real-time drilling data, improving well control and reducing operational risks.

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Area of Science:

  • Petroleum Engineering
  • Machine Learning Applications
  • Drilling Operations Optimization

Background:

  • Equivalent Circulating Density (ECD) is crucial for well control, preventing formation fracturing and circulation loss.
  • Conventional ECD determination methods (downhole tools, mathematical models) are costly, impractical, or lack accuracy.
  • Accurate ECD prediction is essential for safe and efficient drilling operations.

Purpose of the Study:

  • To develop and evaluate machine learning models for accurate ECD prediction using only drilling data.
  • To compare the performance of Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference Systems (ANFISs) for ECD estimation.
  • To introduce a novel equation for real-time ECD determination from drilling parameters.

Main Methods:

  • Utilized drilling data from a horizontal well section, including penetration rate, rotation speed, torque, weight on bit, pumping rate, and standpipe pressure.
  • Developed and trained ANNs and ANFIS models using a dataset of 3570 data points.
  • Validated model performance using an independent dataset of 1130 measurements and assessed accuracy using correlation coefficient (R) and Average Absolute Percentage Error (AAPE).

Main Results:

  • Both ANN and ANFIS models demonstrated strong predictive capabilities for ECD.
  • The ANN model achieved a high coefficient of correlation (R > 0.98) and a very low average absolute percentage error (AAPE) of 0.3%.
  • The ANFIS model recorded a coefficient of correlation (R) of 0.96 and an AAPE of 0.7%, indicating excellent performance.

Conclusions:

  • Machine learning techniques, particularly ANNs and ANFISs, are highly effective for accurate real-time ECD estimation from drilling data.
  • The developed models offer a more accurate and practical alternative to conventional ECD determination methods.
  • The study provides a foundation for improved well control and operational efficiency through advanced data-driven approaches.