Investigations on the prediction of fault occurrences in an oil pipeline transportation system using integrated IoT module with data clustering algorithm

Abstract

In the modern era, oil and gas serve as the essential energy source available to human life. Due to the upgradation in urban construction and industrial technologies, more pipelines over long distance have been established to meet the energy demands. The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, dents, corrosions and cracks due to the contact of oil particle with the pipeline surface walls affecting the reliability of the pipeline system. During oil transportation through pipelines, a no-slip condition exists, causing maximum pressure rise resulting in a reduced flow rate of oil flow. A flow rate gets disturbed at the destination by decreasing the production rate due to variation in the pressure of the transporting oil. Hence pressure and flow rate are the most important and influenced parameters to evaluate risk occurrences rather than pour point, flash point, viscosity, temperature, etc. in the oil pipeline system. It is important to concentrate on monitoring and control of the oil pipeline transportation system before the failure resulting in fatal accidents and environmental pollution. At present, remote monitoring and control of the oil pipeline system are handled by SCADA (Supervisory Control and Data Acquisition) and smart pig. Smart pig is an instrument traveling inside the oil pipeline for inspection and cleaning. Information obtained through smart pig indicates maintenance and repairing steps to be taken to resolve the pipeline problems. newline