Developing a Digital Twin Data Architecture for the Niger Delta Pipeline Network

Digital twin technology applied to pipeline network data can significantly improve operational monitoring, leak detection, and maintenance planning in Nigeria's critical oil and gas infrastructure. This study developed a digital twin data architecture for a 340-kilometre pipeline network segment in the Niger Delta operated by a national oil company. A professional design methodology was applied with structured consultations with 12 pipeline integrity engineers and 10 industrial data architecture specialists, review of published pipeline digital twin deployments by Shell, Equinor, and BP, and technical assessment of the existing SCADA data infrastructure on the network. The architecture designed specifies IoT sensor data ingestion with 30-second polling frequency for pressure, flow, and temperature, a time-series database using InfluxDB for real-time storage, a physics-informed simulation layer for anomaly context, a machine learning anomaly detection engine for leak and intrusion events, and a 3D visualisation interface for control room operators. Data latency requirements below two minutes for alarm generation and integration with existing NICIS pipeline monitoring protocols are specified. Expert review by eleven pipeline engineering and data science specialists confirmed the architecture's technical and operational feasibility. The study recommends piloting on a 40-kilometre test segment with a 6-month proof-of-concept, establishing a joint operations and data science team, and progressing toward DNV certification of the digital twin model for regulatory acceptance.

Keywords: digital twin, pipeline monitoring, Niger Delta, IoT data architecture, anomaly detection