Classical and machine learning modeling of crude oil production in Nigeria: Identification of an eminent model for application

Abstract

The production and exportation of petroleum plays a dominant role in Nigeria’s economy. A drastic fall in the quantity of crude oil produced is fatal to the Nigerian economy since Nigeria depends heavily on the oil sector. Different classical statistical methods have been used to model the crude oil production but the application of machine learning models on crude oil production has been grossly understudied. In this study, we identified a high-performance model between a classical statistical model (ARIMA) and two machine learning models (ANN and RF) in modeling the crude oil production in Nigeria. The monthly data on crude oil production in Nigeria collected from January, 2006 to October, 2020 used in this study is secondary data from Nigerian National Petroleum Corporation (NNPC) and Reuters. The ANN model has a significant modified Diebold–Mariano test when compared with the ARIMA and RF models in the test set. The ANN model had the best trade-off of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Nash–Sutcliffe Efficiency (NSE) values and was used for forecasting crude oil production in Nigeria. The crude oil production is predicted to increase slightly from March, 2021 (1.67 mmbd) to September, 2021 (1.685 mmbd) before it begins a downward trend from October, 2021 (1.684 mmbd) till it gets to 1.628 mmbd in December, 2023. This predicted fall in the quantity of crude oil produced will obviously affect the Nigerian economy since Nigeria depends solely on the oil sector. There is need to diversify the monoculture economic system in Nigeria to explore and harness untapped natural resources.