EN 
BR Data-driven forecasting of oil & gas production: a recurrent neural networks approach
Abstract
Our main objective is to develop and compare two data-driven forecasting models based on Recurrent Neural Networks (RNN). We built the models with the Long Short-Term Memory (LSTM) and the Gated Recurrent Units (GRU). The data was obtained from a synthetic benchmark (UNISIM-II-H), which simulates an Oil & Gas (O&G) pre-salt production field. We performed the training/testing procedures: we trained the models with 80% of data from the same well and tested with the remaining data. Forecasting is calculated using as input the historical record of production variables (liquid, oil, gas & pressure). We also measured the symmetric mean absolute percentage error (SMAPE) to compare our forecasting with the data from the selected benchmark. Several experiments were performed; we used 28, 45, and 90 days for historical records, with 7, 15, and 30 days for forecasting, respectively—most of the experiments exhibited and SMAPE lower to 20. Results from the RNN models exhibited relative values compared to the expected data from the benchmark in most experiments for oil & gas production values.
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VictorEduardo Martinez Abaunza.