Smart Surveillance and Optimization of Artificial Lift Wells Using High-Frequency Data

Abstract

This study introduces an exception-based smart surveillance system that uses a cloud-based data platform integrated with real-time data and steady-state well and surface network simulation results to monitor production and injection operations. Exceptions trigger alarms when predefined rules are violated, helping operators diagnose well behavior. Integrated dashboards display real-time operational trends for detailed analysis and prompt user action. This approach ensures that potential issues are swiftly addressed, minimizing downtime and optimizing production efficiency. To process millions of real time operational data points, a cloud-based data platform stores, contextualizes, and transforms data into the required format. Next, an automated workflow retrieves real-time and model-derived parameters, flagging violations against predefined rules based on operating guidelines or user-defined tolerances. Alarms are triggered by rule violations, low production, data issues, well integrity problems etc., with smart tickets with recommendations assigned to relevant teams. Operators can filter alarms by KPI categories and review detailed data, rule logic, and trends for validation on the dashboard, enabling swift responses while ensuring regulatory compliance and operational efficiency. This solution transforms traditional field management by implementing an exception-based surveillance approach, providing real-time insights through interactive dashboards. The automation of rule-based monitoring and alarm generation significantly reduces response times, enabling faster decision-making and proactive issue resolution. By eliminating the need for manual data monitoring, engineers and operators can focus on high-priority tasks, thus improving operational efficiency and reducing downtime. Moreover, the system enhances collaboration by automatically generating smart tickets with actionable recommendations, ensuring timely resolution by the appropriate teams. Structured alarm classification allows operators to prioritize high-severity alerts, improving overall field performance. From a financial perspective, the solution delivers measurable savings by automating labor-intensive tasks, reducing reliance on manual monitoring, and accelerating issue resolution. This solution enhances monitoring through an automated exception-based approach. By contextualizing and validating real-time data, it improves early detection of well behavior changes like integrity issues, production decline, data inconsistencies, thus reducing false alarms, and improving operational response. The system's adaptability also allows continuous optimization based on evolving field conditions, creating a resilient framework that boosts production performance, asset integrity, and risk management, ultimately fostering sustainable operations.