Rosneft’s Tyumen-based Scientific Institute has unveiled RN-Lab, a corporate information system designed to streamline laboratory workflows for core samples and formation fluids. The system, described by Rosneft’s press service, represents a significant step in digitizing the exploration and production lifecycle by automating key laboratory tasks and enabling tighter data management across projects.
According to Rosneft, RN-Lab brings advanced capabilities to the laboratory floor. It not only identifies sedimentary rock types with greater consistency but also analyzes photographic records to recognize a range of objects, including fossil remnants from ancient organisms. This kind of automated recognition supports researchers by delivering structured results that feed into the system’s library for ongoing improvements. The overarching aim is to build a robust data backbone so the computer vision models can learn to identify core features with increasing accuracy over time.
The RN-Lab project is a core element of Rosneft’s broader modernization agenda, which aligns with the Rosneft-2030 strategy aimed at expanding technological leadership within the oil and gas sector. Rosneft views continuous innovation and the adoption of cutting-edge technologies as central to sustaining competitive performance and energy security.
The RN-Lab platform functions as a digital twin of the laboratory environment, mirroring processes, workflows, and data flows in a unified digital space. The goal is to reduce manual labor, minimize cycle times, and improve the reliability of experimental outputs. Rosneft notes that automation of routine tasks translates into measurable efficiency gains—estimates indicate a 3-5 percent uplift in overall laboratory productivity when core and formation fluid analyses run through RN-Lab. The system architecture comprises multiple integrated modules that collectively cover the spectrum of core analysis and formation fluid examination. To date, eight distinct modules have been developed, each focused on different stages of sample handling, measurement, data capture, interpretation, and reporting.
In practical terms, RN-Lab’s impact on staffing and workflow is substantial. When operating full-size cores, the automation of repetitive steps can lead to labor cost reductions of up to a quarter, delivering tangible economic benefits alongside enhanced data quality and traceability. Rosneft emphasizes that the system not only accelerates tasks but also improves accuracy, reproducibility, and auditability, which are crucial for regulatory compliance and project transparency. The digital twin approach ensures that researchers can simulate, validate, and optimize experiments within a controlled digital environment before applying changes to real-world workflows.