A time series forecasting method for oil production based on Informer optimized by Bayesian optimization and the hyperband algorithm (BOHB)

Abstract

Oil production forecasting is essential in the petroleum and natural gas sector, providing a fundamental basis for the adjustment of development plans and improving resource utilization efficiency for engineers and decision-makers. However, current deep learning models often struggle with long-term dependencies in long time series and high computational costs, limiting their effectiveness in complex time series forecasting tasks. This paper introduced the Informer model, an enhancement over the Transformer framework, to address these limitations. For evaluation and verification, the Informer model and reference models such as CNN, LSTM, GRU, CNN-GRU, and GRU-LSTM were applied to publicly available time-series datasets, and the optimal hyperparameters of the model were identified using Bayesian optimization and the hyperband algorithm (BOHB). The experimental results demonstrated that the Informer model outperformed others in computational speed, resource efficiency, and handling large-scale data, showing potential for practical applications in the future.