Probabilistic Well Production Forecasting in Volve Field Using Temporal Fusion Transformer Deep Learning Models

Abstract

AbstractAccurate well rate forecasting is critical for field development. In the oil industry, recurrent-based deep learning models have been used for production forecasting. Modern research is shifting towards using the novel transformer architecture in natural language and time series applications to better handle long-term temporal dependencies using attention layers. In this paper, we present a novel approach of applying a transformer-based deep learning model for probabilistic production forecasting.The Temporal Fusion Transformer (TFT) model, a modern transformer-based model for time series forecasting, was used to provide a better oil rate prediction in the Norwegian Volve field. The historical bottomhole pressure, wellhead pressure, wellhead temperature and choke size opening real time series data were used as past input features in the TFT model to forecast ahead the oil rate of two wells. The model was trained with a quantile loss function to produce a probabilistic prediction with both upper and lower bounds of uncertainties. After optimization the TFT model was blindly tested on the last 20% of the data to evaluate its prediction performance compared Block Recurrent Neural Network architectures (BlockRNN).The real-time production data used in this multivariate forecasting problem was found to be complex with no clear trend or seasonality. The BlockRNN model failed to produce a good prediction of oil rate compared to the TFT model. The TFT model was better at encoding the input features including, the choke size, wellhead pressure, and temperature, and understanding their long-range dependencies with the oil rate.The model was able to minimize the testing Mean Squared Error (MSE) for the two wells F-11H and F-12H reaching values of 0.08 and 0.03, respectively. In addition, the model was able to forecast a prediction bandwidth in between the 10th and 90th quantiles to account for uncertainty ranges which laid in-between the blind test intervals. Overall, the TFT model was proven successful in accurately forecasting complex trends of oil rates overcoming the limitations of conventional, BlockRNN uses a memory to understand and make predictions about new data models of information loss over long-term multivariate time series prediction.Our work presents a novel approach of using the TFT probabilistic deep learning model for multivariate oil rate forecasting in the oil and gas industry. The model showed very promising results outperforming conventional BlockRNN-based models in addition to providing a range of the forecasting uncertainty using quantile regression. Knowing the uncertainty range helps in making critical decision particularly in the well intervention and field development.