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KGPM: A Knowledge-Guided Predictive Model for Virtual Metering in Gas Wells.

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Summary
This summary is machine-generated.

This study introduces a Knowledge-Guided Predictive Model (KGPM) to improve Virtual Flow Metering (VFM) in smart oil and gas fields. The KGPM enhances data-driven models by integrating prior knowledge, leading to more reliable real-time production predictions.

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Area of Science:

  • Petroleum Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Effective data utilization is crucial for smart oil and gas field development.
  • Virtual Flow Metering (VFM) offers real-time production data without extra sensors, using pressure and temperature data.
  • Current data-driven VFM models struggle with interpretability and output reliability.

Purpose of the Study:

  • To enhance the reliability of data-driven Virtual Flow Metering (VFM) models.
  • To propose a hybrid model integrating prior knowledge into data-driven approaches.
  • To develop a Knowledge-Guided Predictive Model (KGPM) for VFM applications.

Main Methods:

  • Developed a hybrid model by incorporating prior knowledge as a constraint into a Long Short-Term Memory (LSTM) neural network.
  • Guided data-driven model training using this prior knowledge.
  • Established a Knowledge-Guided Predictive Model (KGPM) specifically for VFM.

Main Results:

  • The KGPM demonstrated exceptional proficiency in flow rate prediction.
  • Achieved a Mean Absolute Percentage Error (MAPE) of 3.211% for one experimental well.
  • Achieved a MAPE of 1.141% for a second experimental well.

Conclusions:

  • The proposed KGPM significantly enhances the reliability of VFM.
  • This research contributes to optimizing VFM techniques for intelligent oil and gas fields.
  • The KGPM offers a more interpretable and reliable alternative to purely data-driven VFM models.