Innovative Monitoring Technologies for Well Control Through Sensor Integration and Edge Computing

Abstract Effective monitoring of well performance is vital for optimizing production and operational management in the oil and gas industry. This study presents advanced monitoring technologies integrating digital twins, multisensor networks, and edge computing to enhance real-time data processing capabilities. Laboratory experiments verified the effectiveness of clamp-on vibration sensors over piezoelectric accelerometers, particularly in high-gas-factor scenarios where the latter's performance is severely impaired due to bubble-induced signal disruption. A hybrid measurement system integrating vibration, pressure, and temperature sensors was developed, allowing comprehensive and accurate real-time monitoring of well conditions, equipment anomalies, and the presence of mechanical impurities. The incorporation of mobile measurement units integrated with digital twin technology enables significant reduction in reliance on stationary installations, thus minimizing capital expenditures and operational downtime. A sophisticated data analysis methodology utilizing machine learning and spectral analysis of sensor signals was established, enhancing the accuracy of digital twins and facilitating virtual flow metering. Edge computing technologies enable rapid data processing at the point of acquisition, significantly reducing data transmission volumes and improving responsiveness to operational changes. The proposed system offers considerable potential for practical application by providing precise, non-invasive, and adaptable monitoring solutions capable of enhancing decision-making, reducing operational risks, and improving economic efficiency in complex oilfield environments.