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Digitising Russian downstream projects

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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 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.

Gazprom Neft

Gazprom Neft has introduced a range of successful digitalisation projects including the implementation of blockchain technologies, AI systems, predictive analysis systems, and the Industrial Internet of Things (IIoT). The creation of digital twins for wells, drilling sites, and refining facilities has also proved highly effective.

The company has set digitalisation as one of its priorities by establishing a new Digital Transformation Directorate in April 2018. The main goal of the department is to develop a system covering the company’s digital strategy aimed at improvement of the operational efficiency of all business processes. As well as the development of the company’s own IT platforms for creation of new information flows and databases, and its own predictive analysis tools. The Digital Transformation Directorate is responsible for correlating the data on the company’s progress in the introduction of cutting-edge technologies to work out a strategy for further development.

Earlier, Gazprom Neft had established several company units responsible for the downstream digital technologies. One of them is the Digital Innovation Center focused on developing a technological platform for Gazprom Neft’s business in terms of logistics, processing, and sales. It is aimed at the integration of all elements of the value chain into a united digital space in order to provide more flexible and efficient process management through predictive analysis and real-time data usage. The Center is focused on developing and applying Big Data, blockchain technology, predictive management, digital twin technology, the IoT, and AI-based self-learning tools.

To automate the supervision of efficiency and automation systems performance at the refineries, the company introduced the Monitoring and Diagnostics Center, which delivers the automation systems’ intelligence. It provides a steadily efficient performance and solving of problems concerning equipment operational readiness improvement, a breakdown rate decrease and reduction in repairs, and maintenance costs optimisation. The department was first established in 2015 at the Omsk Refinery and later in 2017 at the Moscow Refinery.

Gazprom Neft uses its Performance Management Center in Saint Petersburg to collect the data from the sensors at all of the company’s refineries in real-time and supervise the efficiency during the whole value chain. Now the Center is continuously processing the data from 250 000 resources. By 2020 this number will increase up to 1 million, allowing it to observe 98% of production characteristics (statistics from

SIBUR Holding

SIBUR Holding took up automation and digitalisation several years ago. In 2012 it introduced the manufacturing execution system (MES) at its production units. This system automates production collecting of data for analysis and allows it to see and control the process in real-time. It requires a primary level of automation and presence of an automated process control system (APCS) in order to take this data into the MES. APCS uses thousands of sensors around the plant to collect data on temperatures, pressure, flows, etc. This information is stored and analysed so that the MES can develop the most efficient and cost-saving production strategy and ensure the high quality of the product.

SIBUR introduced the manufacturing system, also defined as lean production. Now it uses Six Sigma strategies, allowing it to control and improve the quality of products during the refining. It measures the current state and developing processes to decrease the number of possible failures, it works on improving the production by detecting and eliminating the causes of defects, and also minimising variability in manufacturing and business processes.

Automation is also implemented in the HSE sphere. All the data on accidents, even minor ones, is collected in information systems to be analysed, the origin of the accident detected, and used to prevent such cases in the future.

The Holding is constantly experimenting with new digitalisation solutions. For example, in the field of virtual and augmented reality, in order to make emergency response more effective and immediate by involving distant employees. Automation can also be widely used in logistics and transportation to ensure a continuous supply of raw material and products between more than 20 units of the production structure.

In the frames of digital transformation, SIBUR is also going to implement a wide scale of data collection, storage and processing technologies, drones, and 3D printing. As part of this transformation, facial recognition systems have already replaced pass entry systems at the SIBUR units.

The rapid development of automation and digital transformation causes downstream companies to look for innovative solutions that are competitive in the international market but still adapted to the domestic one, as well as compatible with already existing business and technical processes. In such conditions, it is important to have the opportunity to network with the companies from your industry; to exchange the experience, negotiate, and find reliable partners. PRC Russia & CIS 2018 Congress brings together the whole downstream value chain to cover different aspects of modernisation. Gazprom Neft, SIBUR, TechnipFMC, KBR, Shell, Honeywell and other leading companies will gather to discuss real cases and possible further development. There will also be an exhibition, highlighting the innovations and technologies of tomorrow.

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

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