A Conceptual Framework for Optimizing Reservoir Management Using Big Data Analytics and Predictive Maintenance

Effective reservoir management is critical for maximizing resource recovery, ensuring operational efficiency, and promoting sustainability in modern energy systems. This paper presents a conceptual framework for optimizing reservoir management by integrating advanced technologies, including big data analytics and predictive maintenance. The framework encompasses core components such as comprehensive data collection, robust processing mechanisms, and actionable insights, which collectively enable proactive decision-making and enhanced system performance. Predictive models within the framework anticipate system behaviors, allowing operators to mitigate risks, reduce downtime, and optimize resource utilization. Furthermore, the paper highlights the transformative potential of this approach, addressing long-standing challenges such as cost inefficiency, environmental impact, and operational safety. Practical recommendations for industry stakeholders include phased implementation, workforce training, and cross-departmental collaboration to facilitate adoption. By providing a unified structure for reservoir optimization, this framework offers a sustainable and adaptable solution to meet the demands of contemporary energy operations.