Application of Particle Swarm Optimization Technique in Monitoring of Oil Theft and Oil Facility in the Nigeria Niger Delta

Аннотация

In recent times, there have been an increase in crude oil theft and  an upsurge of incessant threats and sabotage to oil and gas facilities in the Niger Delta by self acclaimed militant groups, host community agitation and offshore piracy in the region. The consequences of this act have resulted to huge loss of revenue for the Federal Government of Nigeria and major disruptions to gas supply to power electricity for industry, businesses and public sector services. However, this situation can be averted if efficient predictive monitoring systems are put in place.  Many algorithms have been developed in recent years for solving problems of predictive classification in oil and gas field. Most promising among them are swarm intelligence algorithms which is emerging as an alternative to more conventional techniques for predictive monitoring operation. In this paper a Particle swarm optimization technique (PSO) has been proposed for prediction and monitoring against crude oil theft and security of oil and gas facility. Dynamic simulations were performed using real time dataset of most likely vandal's behaviour to test and evaluate the system performance. The result of the simulations study showed impressive results and proves PSO as a better model for diverse kinds of predictive task.