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Digitising Russian downstream projects - part one

Published by , Senior Editor
Hydrocarbon Engineering,

Technologies such as Internet of Things (IoT), cognitive computing, remote sensing, data combination, etc are developing in global upstream, midstream, and downstream segments. In 2017, automation and digitalisation have seen a great boost in investments by industry leaders such as Statoil, BP, GE Lukoil, Gazprom Neft, etc. These innovations have many applications in the downstream sector. For example, in the refining process itself, smart plant and digital twin technologies can be applied, or end-user services for B2B and B2C markets, such as Shell’s catalogue virtual assistant based on artificial intelligence (AI).

This article, published in three parts, will observe the latest experiences of three Russian oil and gas giants in implementation and support of Industry 4.0 innovations.

The first company under the spotlight is Tatneft.


Most of the latest innovations at Tatneft’s TANECO refinery were implemented in the frames of a long-term partnership with Kaspersky Lab. They are aimed to improve security systems and to boost the operational efficiency of the plant.

One of the crucial TANECO projects is digitalisation of a crude distillation unit ELOU-AVT-7. The unit is sensitive to oil quality, cooling system disruptions, internal temperature, and other factors. Its functioning has an impact on the quality and effectiveness of further refining, and the company invests a lot in the security and continuity of the technical process. The technologies allow them to retrieve information on all abnormalities, prevent emergencies, and therefore cut costs on the recovery.

The main technology that ensures refining continuity of the unit is an early anomaly detection system launched in July 2018. It is based on real-time machine learning for anomaly detection (MLAD technology), developed by Kaspersky Lab. MLAD gets the information on the technical process from the neural network built in 2017. If the system detects data within normal parameters from the pre-programmed database, it is able to 'learn' and consider them in future. It is more a flexible and effective solution than an expert system diagnostics working according to the defined rules. Since its test run, the anomaly detection system has compiled information on different kinds of anomalies: technological process issues related to the periods of the regime change; control loops transition to manual mode; and issues caused by incorrect data from sensors.

The issues in the refining process may be caused by an accident such as depreciation of the equipment and human error, or by external factors, e.g. a cyber attack on the refinery. In August 2018, Kaspersky Industrial CyberSecurity and TANECO successfully tested a new cybersecurity complex, which is combined with a telemetry system based on machine learning and AI in order to prevent both cyber crimes and human factor errors. The neural network registers all the data on the technical process and learns from it. Thus the technology is able to predict the status of the system for the next few minutes and localise any anomalies immediately before the issue occurs. It minimises the risks and the losses.

Kaspersky Lab and TANECO’s collaboration allowed them to launch the Kaspersky Lab ICS CERT initiative (Industrial Control Systems Cyber Emergency Response Team). ICS CERT is designed to provide security to the industrial structures and crucial objects. Its main goal is to coordinate the actions of the cyber security solution providers, automation equipment manufacturers, owners and operators of the plants, as well as researchers in the field of cybersecurity. CERT will form a database of vulnerabilities, incidents and potential threats detected, and provide recommendations on cyber security improvements from the collected experience. All data, excluding confidential material, will be publicly available anonymously, only after the revealed security threats are eliminated.

The latest TANECO digital update, announced on 19 June 2018, is the digital twin of ELOU-AVT-7, which is being implemented in collaboration with ChemTech company. All the preliminary steps have been made. The processing of the data from several years of unit production, the creation of the production thermodynamic model, the development of virtual analysers predicting the composition of process flows, and the planning of the process regime optimisation. Processing and analysis of the large amount of data was carried out based on the Azure platform in partnership with the Microsoft Company.

Written by Kristina Sabirova, Managing Partner of BGS Group and Project Director of PRC Russia & CIS 2018.

This is the first part of a three part article. The second part will outline how Gazprom Neft is implementing Industry 4.0 innovations.

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Downstream news