Comparing ARIMA and LSTM models to predict time series in the oil industry - Correia, Jaqueline B.; Pivetta, Marcos; do Nascimento, Givanildo Santana; Becker, Karin (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2021)
Monitoring and forecasting oil and gas (O&G) production is essential to extend the life of a well and increase reservoirs' productivity. Popular models for O&G time series are ARIMA and LSTM recurrent networks, and tipically several lags are forecasted at once. LSTM models can deploy the recursive prediction strategy, which uses one prediction to make the next, or the multiple outputs (MO) strategy, which predicts a sequence of values in a single shot. This work assesses ARIMA and LSTM models for the forecasting of petroleum production time series. We use time series of pressure and gas/oil flow from actual wells with distinct properties, for which we developed predictive models considering different time horizons. For the LSTM models, we deploy both the recursive and MO strategies. Our comparison revealed the superiority of LSTM models in general, and MO-based models for longer time intervals.