Innovative intelligent technologies for predictive reliability and risk management in oil and gas transport and storage systems

Abstract

The article proposes algorithmic, mathematical complex based on neural networks for the assessment and prediction of the reliability and safety of station equipment using diagnostic data. The paper shows that despite the complexity and multifactor nature of functioning process of main transport energy-mechanical equipment and the distribution of oil, oil products and gas of various types, the reliability of the equipment can be estimated and predicted using the apparatus of neural networks during the operation in various modes. It is noted that a modern automated process control system makes it possible to create an operational data bank in real time at various hierarchical system levels, which can be successfully analyzed using artificial intelligence technologies. The article discusses some aspects for applying the developed methodological complex for specific objects of oil and gas transportation industry.