Animesh Pradhan

From Una in Himachal Pradesh to Tuticorin in Tamil Nadu and Mundra in Gujarat to Guwahati in Assam, the 15,113 km based massive network of pipelines in IndianOil is engaged in the safe and sustainable transport of crude oil, petroleum products and natural gas. Apart from the natural vulnerabilities, including corrosion and leakages, the pipelines are also subject to various intrusions by miscreants in an attempt to steal products. Each of these vandalism attempts could account for damages in crores. Apart from economic losses, any pilferage-led spill endangers human lives and the ecological viability of the surroundings, along with threatening the energy security of the nation. Catering to the prevention needs of pipeline integrity against pilferages, a machine learning-based model, the Pipeline Vulnerability Management System (PVMS), has been built. PVMS is an ensemble-based classification model that identifies certain stretches of pipelines to be more vulnerable (Red Zones) to pilferage attacks than the rest. The limited security personnel can be optimally deployed in the Red Zones to prevent pilferage events. The solution assures extensibility to various other industrial applications (Heatmapping Conveyer belt maintenance, logistic supply chains in FMCG), and further research can help enhance assessment at a more granular level.