Rosneft Expands Advanced Field Pipeline Monitoring System
Rosneft's engineering specialists have upgraded the company's RN-SMT field pipeline monitoring system, extending centralized digital oversight across more than 70,000 kilometers of production pipelines.
The system consolidates information on pipeline routes, asset characteristics, maintenance history, diagnostic results, and the composition of transported fluids. Every day, it automatically evaluates the integrity of individual pipeline sections, calculates throughput capacity, identifies potential failure risks, and prioritizes assets requiring attention. It also recommends either repairing a pipeline section or replacing it, taking both technical and economic considerations into account.
An All-Seeing Digital Eye
Data from production sensors flows into the company's unified digital environment. That enables engineers to identify potentially hazardous pipeline sections more quickly and shift from scheduled maintenance toward condition-based maintenance driven by the actual state of the infrastructure. Notably, this is not a pilot deployment at a single oilfield but the continued expansion of a centralized system covering the pipeline infrastructure of the core upstream assets operated by one of Russia's largest oil companies.
The History of RN-SMT
The system was developed in 2022 by specialists at RN-BashNIPIneft, Rosneft's Ufa-based engineering and design institute, which is part of the company's research and engineering division. The project originated within Rosneft's corporate program to improve pipeline infrastructure reliability. Initially, deployment was planned on a limited scale at Bashneft-Dobycha, RN-Yuganskneftegaz, and Samotlorneftegaz. At that stage, the software analyzed transported fluid properties, corrosion rates, and diagnostic inspection data.
In 2023, Rosneft Oil Company began preparing the system for full-scale deployment, which was scheduled for completion in 2024. At the same time, the company tested an automated integrity monitoring system for pressurized oil pipelines capable of determining the precise location of pipeline breaches. Additional data sources were incorporated into the platform, expanding its ability to detect early indicators of pipeline degradation.
Before RN-SMT was introduced, pipeline monitoring was often fragmented. Sensor data was collected from separate sources, while maintenance decisions relied largely on periodic inspections, making it difficult to identify emerging problems at an early stage. The new platform marked a significant step toward the digital transformation of pipeline transportation by integrating information throughout the entire asset life cycle, from engineering design and route planning to final decommissioning.
Delivering Full Performance
The unified digital environment now receives data not only from production sensors but also from specialized monitoring instruments. These include probes that detect biofilms forming on pipe walls and continuously measure corrosion rates. The automated assessment and prioritization engine evaluates not only technical parameters such as wear, pressure, and corrosion but also additional operational factors. One of the key enhancements introduced beginning in 2024 enables the system to calculate pipeline throughput in real time using current operating parameters from both production and injection wells.
The RN-SMT project is an important component of Rosneft's broader strategy to achieve technological leadership. Rosneft has established itself as one of the first companies in Russia to systematically develop proprietary high-technology software across all core business processes. Today, its software portfolio includes dozens of digital products, some of which have already entered external markets. The updated version of RN-SMT is expected to generate an economic benefit exceeding 1 billion rubles (approximately $13 million). The continued development of systems like RN-SMT also supports the company's long-term Rosneft-2030 strategy, which identifies technological leadership as a key driver of competitiveness.
Learning Contiues
Looking ahead, experts expect the platform's analytical capabilities and machine learning functions to expand further. The system will learn from large volumes of historical operating data to improve risk prediction accuracy and recommend preventive maintenance measures more effectively.
Integration with other digital solutions across the company will also be essential. These include digital twin technologies and drone-based monitoring systems. Additional functional modules may eventually be introduced to support deeper environmental risk analysis and optimize transportation logistics. Overall, the modernization of RN-SMT represents a clear evolution from a centralized database into an engineering and operational decision-support platform.
