Time-series forecasting of crude oil production using hybrid modeling

Abstract

Crude oil is the main energy source, and its demand has been usually growing over years. It has always been an issue in the petroleum industry to forecast the production of crude oil to avoid disruption of supplies and keeping the prices of oil and commodities in control and thereby manage inflation. Hence, it becomes crucial to predict the production of crude oil. This study uses time series data to forecast crude oil production. Traditional statistical Autoregressive Integrated Moving Average (ARIMA). model and deep learning models like Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Gated Recurrent Unit (GRU) are used for prediction and comparison. A hybrid technique is used to develop an ARIMA-ANN model to forecast crude oil production more accurately