Integrating Machine Learning with Intelligent Control Systems for Flow Rate Forecasting in Oil Well Operations
Amangeldy B. Tasmurzayev N. Shinassylov S. Mukhanbet A. Nurakhov Y.
September 2024Multidisciplinary Digital Publishing Institute (MDPI)
Automation
2024#5Issue 3343 - 359 pp.
This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Momentum LSTM (MLSTM), on a dataset of 21,644 operational records. These models were trained to predict a critical operational parameter, FlowRate, which is essential for operational integrity and efficiency. Our results demonstrate substantial improvements in predictive accuracy: the LSTM model achieved an R2 score of 0.9720, the BiLSTM model reached 0.9725, and the MLSTM model topped at 0.9726, all with exceptionally low Mean Absolute Errors (MAEs) around 0.0090 for LSTM and 0.0089 for BiLSTM and MLSTM. These high R2 values indicate that our models can explain over 97% of the variance in the dataset, reflecting significant predictive accuracy. Such performance underscores the potential of integrating ML with SCADA systems for real-time applications in the oil and gas industry. This study quantifies ML’s integration benefits and sets the stage for further advancements in autonomous well-monitoring systems.
bidirectional LSTM , flow rate prediction , GRU , LSTM , machine learning , oil and gas industry , oil well monitoring , operational efficiency , predictive maintenance , SCADA systems , time series forecasting , transformers
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Joldasbekov Institute of Mechanics and Engineering, Almaty, 050010, Kazakhstan
Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty, 050010, Kazakhstan
Department of Information Technology, Astana IT University, Astana, 050040, Kazakhstan
Joldasbekov Institute of Mechanics and Engineering
Faculty of Information Technologies
Department of Information Technology
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