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Gazprom Neft’s Downstream Efficiency Control Centre goes into operation

Gazprom Neft has launched its Downstream Efficiency Control Centre unique throughout the industry in St Petersburg, directed at improving business efficiency through the use of cutting-edge technologies in data analysis, predictive analytic methodologies*, and managing “big data”. The strategic objective of this project is to build a single and inclusive digital platform for controlling efficiency throughout the value chain — from oil delivery to refining facilities, to end-user sales of petroleum products.

Gazprom Neft’s Downstream Efficiency Control Centre

The Gazprom Neft project is, so far, without precedent anywhere in the world. A single, inclusive technological platform for managing efficiency downstream involves creating a “digital twin” of the entire value chain,** based on predictive analysis tools, neural network technologies, and artificial intelligence. The operational concept of the Downstream Efficiency Control Centre is built around the integration of various value chain management systems, ensuring free and continuous data exchange between these and using predictive analysis methodologies on indicators including demand for oil products, equipment reliability, oil-product quality, environmental monitoring, energy efficiency, and so on. 250,000 sensors, and dozens of systems, transmit information to the Downstream Efficiency Control Centre in real time from across all of those company assets that form part of Gazprom Neft’s Logistics, Processing and Sales Division. The Downstream Efficiency Control Centre will be processing incoming data from control instruments and sensors used in the automated monitoring of production processes covering 90 percent of process-dependent variables and material flows, in continuous operation. Monitoring and analysing the volume and quality of raw hydrocarbon materials and finished products at every stage of the value chain allows potential sources of deviation to be identified.

The development of precision-engineered models of technical installations — digital twins of assets, allowing the transition to the proactive management of plant reliability, safety and efficiency — is part of the ongoing work of the Centre, where models of two facilities created with the assistance of neural network technologies and AI, and capable of independent machine learning through the analysis of large volumes of data are currently undergoing testing.

Anatoly Cherner, Deputy CEO for Logistics, Processing and Sales, Gazprom Neft, commented: “The future of the industry lies in continuous improvements in efficiency and technological development. As part of the digital transformation of the business we are working on the development of a single and inclusive technological platform for efficiency management in oil refining and sales, which will bring together all elements of the value chain, allowing a significant improvement in the company’s operational efficiency. We intend to make every possible use of the new opportunities and tools coming out of ‘Industry 4.0’, confirming our status as an industry leader in technology.”

Notes for editors

* Predictive analysis — from the English “predictive analytics” — refers to a class of methodologies in data management concentrated on predicting the future behaviour of objects and entities in order to optimise decision making.

** A “digital twin” is a dynamic digital model of a physical object or system.