Abstract: 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 typically 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.

Keywords: Time series forecast, Oil industry, LSTM, ARIMA

Authors: Jaqueline Bitencourt Correia, Marcos Vinicius Ludwig Pivetta, Givanildo Santana do Nascimento and Karin Becker

Journal/Conference: Symposium on Knowledge Discovery, Mining and Learning (KDMILE)

Publication: https://doi.org/10.5753/kdmile.2021.17470