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Petroleum is an essential commodity in today’s world for the survival of mankind. The petroleum reserve in the world is limited and it is expected to get exhausted in the coming future, and the world is witnessing the crisis of this oil. The prediction of petroleum crisis in the world is a challenging problem to deal with. In this paper, we are using a novel and suitable time series prediction approach based on artificial neural network (ANN) for the forecast of future instances of a time series data. The prediction of petroleum crisis in the near future is obtained by a nonlinear and multistep method known as nonlinear autoregressive network (NARnet). The data set is obtained from different government sources of ten countries (like USA, Middle East countries, China and India, etc.) for over a period of more than 30 years and contains three features, viz. population, petroleum production, and petroleum consumption. The NARnet model requires known instances which are employed for the dynamic multistep prediction for the ahead time where one can predict as many numbers of future data instances as looked-for. The normalized mean square error (NMSE) and T-test of our prediction method, i.e., NARnet, have been verified by different standard predictive methods to handle the data set and found to be better in its performance. It has been forecasted that by 2050, hydrogen fuel (which can reduce pollution and boost the fuel efficiency at lower cost) could be thought of as an appropriate replacement for petroleum.

Petroleum is being consumed at a rapid pace all over world, but the amount of petroleum is constant in earth crust and production to consumption requirement is not up to mark. It is expected that a day may come when this world will witness the crisis of this oil. For this our paper addresses the prediction of petroleum crisis in two most populated country of the world i.e. India and China using novel Artificial Neural Network (ANN) based approach. The set of observation comprising three features like population, petroleum production and petroleum consumption are being considered to design the predictive model. Our work shows that petroleum production over consumption with respect to sharp increase of population, leads to a decisive issue in production of an alternative fuel like Hydrogen fuel. In our analysis, we used the data provided by different government sources over a period of more than 30 years and then simulated by a multistep ahead prediction methodology, i.e. nonlinear autoregressive Network (NARnet) to predict petroleum crisis in near future. The results of present study reveals that for India, the Normalized Mean Square Error (NMSE) values found for population petroleum production and consumption are 0.000046, 0.2233 and 0.0041 respectively. Similarly for China the corresponding values are 0.0011, 0.0126 and 0.0041 respectively, which validates the accuracy of the proposed model. The study forecasts that by 2050 hydrogen fuel may be a suitable replacement for petroleum, and will not only reduce pollution, but also enhance the fuel efficiency at lower cost as compared to that of petroleum.