A Comparative Study of Deep Learning Models and Traditional Methods in Forecasting Oil Production in the Volve Field

Abstract

Abstract Accurate well rate forecasting is essential for successful field development in the oil industry. Recurrent-based deep learning models have traditionally been used for production forecasting. However, recent advancements in the field have led to the use of transformer and transfer learning to overcome the need for large amounts of data. This paper introduces a novel approach to using state of the art deep learning algorithms for oil production forecasting. To enhance the accuracy of oil rate predictions in the Norwegian Volve field, a combination of statistical models and cutting-edge deep learning models were utilized. These models included Autoregressive Integrated Moving Average (ARIMA), Block Recurrent Neural Network (BlockRNN), Temporal Fusion Transformer (TFT), and the Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) using meta learning. The models used multivariate real-time historical data, such as bottomhole pressure, wellhead pressure, wellhead temperature, and choke size, as input features to predict the oil rate of two wells. The models were trained on 85% of the data and tested on the remaining 15%, with the advanced models TFT and N-BEATS being compared to the conventional models in terms of prediction performance. The complex production data used in this forecasting problem showed no clear trends or seasonality. The state-of-the-art deep learning models, the Temporal Fusion Transformer (TFT) and the Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), outperformed other models in accuracy of forecast. The TFT model was able to significantly minimize the testing Mean Squared Error (MSE) for wells F-11H and F-12H. Additionally, the model predicted a range of uncertainty between the 10th and 90th quantiles to consider the variability in the blind test intervals. The N-BEATS meta learning model was better at capturing dynamic time series features from the M4 dataset and applying that knowledge to predict oil rates in the Norne field, without any input variables like reservoir pressure. The N-BEATS approach was superior to all other models in terms of the difference between the forecast and actual rate, resulting in a mean square error of 0.02 for well F-12 and 0.05 for well F-11 respectively. Our work, to the best of our knowledge, presents a novel implementation of a new model and evaluates the efficiency of deep learning models in forecasting oil production compared to conventional methods. Previously, machine learning and deep learning techniques in the petroleum sector mainly utilized historical field data for their predictions. However, our study highlights the potential of meta learning and the N-BEATS model in greenfield or newly developed areas where historical data are scarce. Additionally, the TFT probabilistic deep learning model showed outstanding results, outperforming traditional models, and providing a range of forecast uncertainty, which is very useful in making well-informed decisions in field development.