Fluid rate quantification is important for field development and well planning. Existing physical and data-driven models require a copious amount of reservoir information and history to improve the capability of prediction. In this paper, we introduce a new approach of using a deep meta learning approach using Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) model to forecast the oil rate, eliminating the need of large historical training datasets to overcome the limitation of conventional models.

Two deep learning models, the Long- Short-Term Memory (LSTM) and N-BEATS, were used to forecast the oil rate data of two wells from Norne Field in Norway. The historical bottom hole pressure data with only 500 data points were used as input in the LSTM model. To overcome the dataset limitation, we developed and tested a pretrained N-BEATS transfer model. The N-BEATS model was initially trained on a large and heterogeneous dataset, widely known as the M4 series, with an excess of 100,000 time series. This pretrained model was then used to forecast the oil rates using a meta learning approach.

It was challenging to fit the LSTM model, as it demanded complex hyperparameter tuning and grid search to solve this nonlinear multivariate time-series forecasting problem. Even after we tuned the model's parameters, the model could not generalize the testing data attributed to the limited and small dataset available during training. Our work showed that the newly proposed N-BEATS meta learning model was better able to capture the dynamic time series features from the M4 dataset and transfer the learned knowledge. The N-BEATS approach was able to forecast oil rates in Norne field for both wells without supplying the model with any features or input variables, such as the reservoir pressure. The Mean Squared Error (MSE) was used as an evaluation metric to benchmark the prediction results from both models. The results clearly demonstrated that the N-BEATS meta learning approach outperformed the LSTM model performance when comparing the difference between the model forecast and actual data. We concluded that using the pretrained N-BEATS model overcomes the previous disadvantages in LSTMs model requiring feature selection and abundant training history.

To the best of our knowledge, our work presents a novel approach of using N-BEATS meta learning model for oil rate forecasting without any prior knowledge of the field. Most previous machine and deep learning attempts were based mainly on deriving predictions from an abundant historical field data. This research showed promising results using meta learning and N-BEATS model in the petroleum industry, especially for new or green fields with limited historical data.

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