Rosneft Engineers Train Neural Network to Assess Oil Pipeline Conditions
Engineers developed the system by combining a neural network with a broad set of mathematical equations describing physical processes inside oil pipelines.
Rosneft hosted a scientific and engineering conference for young professionals in Tomsk, where participants presented a new software platform for monitoring the condition of oil pipelines.
The system is built around a neural network model. Rather than simply collecting operational data, it analyzes information using equations from electrochemistry, hydrodynamics, and heat transfer. This enables the software to generate a comprehensive picture of pipeline health.
The algorithm can display pipeline conditions in real time, including the percentage and rate of wear along the entire pipeline. The software also includes dynamic forecasting capabilities that project operating parameters several years into the future. According to the developers, this approach improves defect prevention and makes maintenance planning more accurate.
The pipeline monitoring platform was one of 150 projects presented during the conference.
