The global industrial market – also known as the Industrial Internet of Things (IIoT) – will be worth $552.7bn by 2029, according to GlobalData research.
The energy sector is expected to generate $79bn in industrial internet market revenue within three years.
Ravindra Puranik, Oil and Gas Analyst at GlobalData, said autonomous operations are rapidly becoming standard and cloud-based analytics and AI systems connect the dots from raw input to final distribution, improving the accuracy of demand forecasting and inventory management even in volatile markets.
The upstream segment is at the frontline of Industrial Internet adoption. Projects are increasingly capital intensive and geographically remote, facing new subsurface challenges and rising environmental, social, and governance scrutiny.
©GlobalData
As a result, real-time monitoring and modelling are now expected, not optional. Digital twins, AI-driven drilling optimisation, and field-wide IoT networks enable operators to simulate outcomes, remotely manage wells, predict equipment failures, and integrate new production faster than ever.
In the midstream segment, sensors placed across pipelines and tanks provide real-time data on pressure, flow, and integrity, enabling faster leak detection and improved responses to anomalies.
In the downstream sector, real-time data collection and advanced process automation now underpin production optimisation and emissions control, among other areas. Digital twins are enabling continuous process modelling, rapid scenario testing, and proactive troubleshooting.
Industrial gases majors such as Air Liquide and Linde use connected sensors to manage huge supply chains, collecting billions of data points daily from thousands of plants, trucks, and gas tanks.
Alongside smart tank monitoring, the technology is instrumental to truck route optimisation, predictive maintenance and energy management.
But IIoT presents challenges too. These include cybersecurity risks, the harsh operating environments typical of gas facilities, difficulties with interoperability across legacy equipment, and complexity of managing massive data volumes.
