Bridging the Air Gap: A Comprehensive Workflow for Integrating Drone Data Into the Digital Oilfield (DOF)

Abstract This paper addresses a critical industry gap: the absence of a standardized, comprehensive framework for integrating diverse drone-collected data into Digital Oilfield (DOF) infrastructures. A detailed, end-to-end workflow is presented to transform raw data from unmanned aerial vehicles (UAVs) into actionable intelligence across onshore and offshore operations while maintaining alignment with industry standards and cybersecurity requirements. The proposed five-stage workflow covering data acquisition, pre-processing, data management and integration, analytics, and application enablement, combines established industry data standards such as WITSML, PRODML, and OSDU with modern drone technologies, cloud platforms, and advanced analytics. Each stage outlines technical specifications, best practices, and NIST-aligned cybersecurity measures to ensure reliability and data integrity across the enterprise. The framework mitigates integration risk through standardized data mapping and quality assurance, supports real-time fusion of multi-modal sensors (visual, thermal, LiDAR, and gas), and enables advanced DOF applications including predictive integrity, anomaly detection, and environmental compliance (methane LDAR). Key integration challenges ranging from cybersecurity and interoperability to data governance are addressed with best-in-class, standards-aligned solutions, enabling safe, secure, and efficient drone-to-DOF operations. Finally, A practical case from ADNOC, integrating drone-based methane detection with DOF workflows, demonstrates the approach's scalability and ESG impact. The result is a vendor-agnostic, secure, and operationally proven pathway for safe, cost-effective, and sustainability-driven digital oilfield operations.