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This study introduces a groundbreaking Autonomous Adaptive Meta Model (AAMM) as an innovative solution to meet the escalating demand for precise and reliable oil prediction rates over a 20-year horizon. By leveraging machine learning algorithms and edge computing techniques, the AAMM dynamically adapts and optimizes its prediction model in real time, responding to changing oilfield conditions. It integrates Extremely Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Long Short-Term Memory (BiLSTM), and Artificial Neural Network (ANN) to autonomously learn and adjust its parameters based on real-time feedback from the oilfield data. This adaptive capability enhances the predictive accuracy and reliability in dynamic and complex oilfield environments. Additionally, the AAMM incorporates edge-computing technologies to process and analyze data directly at the source to reduce latency and expedite decision-making. Utilizing a comprehensive dataset comprising historical oil production data, geological information, well characteristics, and other relevant factors, the AAMM remains up-to-date with the latest information through real-time streaming data integration. Validation and testing on real-world oilfield data demonstrate the AAMM’s superiority over the traditional standalone and static meta models. Its autonomous adaptation is crucial in maintaining accuracy amid changing conditions, providing a robust solution for oil production optimization.

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An Autonomous Adaptive Meta Model (AAMM) for Real-Time Oil Rate Prediction and Optimization in Dynamic Environments

  • 会议论文

  • 发表日期：

    2025年

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  • Fatna A. Said,
  • Zhang Kai,
  • Huaqing Zhang,
  • Johnson J. Kasali

摘要

This study introduces a groundbreaking Autonomous Adaptive Meta Model (AAMM) as an innovative solution to meet the escalating demand for precise and reliable oil prediction rates over a 20-year horizon. By leveraging machine learning algorithms and edge computing techniques, the AAMM dynamically adapts and optimizes its prediction model in real time, responding to changing oilfield conditions. It integrates Extremely Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Long Short-Term Memory (BiLSTM), and Artificial Neural Network (ANN) to autonomously learn and adjust its parameters based on real-time feedback from the oilfield data. This adaptive capability enhances the predictive accuracy and reliability in dynamic and complex oilfield environments. Additionally, the AAMM incorporates edge-computing technologies to process and analyze data directly at the source to reduce latency and expedite decision-making. Utilizing a comprehensive dataset comprising historical oil production data, geological information, well characteristics, and other relevant factors, the AAMM remains up-to-date with the latest information through real-time streaming data integration. Validation and testing on real-world oilfield data demonstrate the AAMM’s superiority over the traditional standalone and static meta models. Its autonomous adaptation is crucial in maintaining accuracy amid changing conditions, providing a robust solution for oil production optimization.