Development of a Smart Monitoring System for Enhanced Oil Recovery Optimization

Thesis Abstract

The efficient and sustainable extraction of hydrocarbons remains a critical challenge in petroleum engineering, particularly amidst the increasing complexity of reservoir dynamics and the exigency for optimized recovery techniques. Traditional monitoring systems in enhanced oil recovery (EOR) processes often lack the real-time responsiveness and adaptive capabilities necessary to maximize extraction efficiency, leading to suboptimal recovery rates and unanticipated operational costs. This study aims to develop a comprehensive, intelligent monitoring system that integrates advanced sensor technologies, data analytics, and decision-support algorithms to optimize EOR processes. The primary objectives include designing a scalable smart monitoring architecture, implementing real-time data acquisition and processing protocols, and validating system performance through simulation and field pilot testing. The research adopts a mixed-methods approach comprising both qualitative and quantitative strategies. The population encompasses upstream oilfield operators, reservoir engineers, and system developers within an operational oilfield with extensive EOR activity, totaling approximately 120 potential participants. A stratified random sampling technique selects 50 engineers and operators for interviews and validation of system usability, while a systematic sampling method is employed to gather operational data from 10 production wells over a 12-month period. Data collection instruments include custom-designed sensor arrays deployed at critical points within the reservoir, digital questionnaires assessing operational parameters, and system logs capturing real-time responses. The system's analytical framework employs advanced signal processing techniques, multiple regression analysis to determine relationships between operational variables and recovery efficiency, and machine learning algorithms, such as support vector machines, for predictive modeling. Key anticipated findings suggest that the integration of adaptive sensor networks with intelligent data analytics can significantly enhance real-time decision-making, leading to measurable improvements in oil recovery rates—projected increases of up to 15% over conventional monitoring approaches. The system is expected to identify optimal injection and production parameters dynamically, reduce operational downtime through predictive maintenance alerts, and facilitate more precise reservoir management. The validation phase, involving both simulation models and field trials, should demonstrate the system's robustness, scalability, and cost-effectiveness, establishing a compelling case for widespread adoption in EOR operations. This research contributes novel insights into the application of smart sensor technologies and artificial intelligence in petroleum recovery processes, filling existing gaps in the literature concerning integrated monitoring systems tailored to EOR environments. The study extends the theoretical foundations of sensor fusion and adaptive control within the petroleum engineering domain, aligning with the Systems Theory and the Theory of Dynamic Optimization as conceptual underpinnings. Additionally, it provides a framework for future research on integrating IoT (Internet of Things) platforms with reservoir management strategies. In conclusion, the designed smart monitoring system is poised to revolutionize EOR practices by enabling proactive, data-driven operational adjustments that maximize hydrocarbon extraction while minimizing environmental impact. Recommendations include further refinement of predictive algorithms, development of user-centric interfaces for operational personnel, and exploration of system scalability for multi-field applications. Future research directions suggested involve incorporating advanced geophysical imaging techniques and leveraging cloud computing for data storage and dissemination. The findings are expected to serve as a valuable reference for industry stakeholders, policymakers, and academia committed to enhancing resource recovery efficiency through innovative technological solutions.