Observational analysis revealed low-latency processing in oil and gas operations, indicating intelligent systems may improve resilience.
The oil and gas industry is undergoing rapid digital transformation, driven by the demand for intelligent systems that enhance safety, efficiency, and sustainability. This study proposes a robust architecture for Edge-enabled Distributed Digital Twins (DDTwins), designed to support operations in remote and connectivity-constrained environments. By integrating real-time sensor data, edge computing, and lightweight ML workflows, the framework addresses key challenges of latency, bandwidth limitations, and intermittent connectivity. At its core, DDTwins leverages a microservices-based ML orchestration platform that enables real-time data processing, low-latency analytics, autonomous decision-making, and resilient performance at the edge under degraded network conditions. A prototype deployment simulating remote gas well operations was implemented on resource-constrained hardware to evaluate feasibility. The system achieved 8 s inference latency, maintained CPU utilization below 40
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Abdel-Rahman et al. (2025) studied this question.
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