The Recurrent Neural Network (RNN) has found extensive application in production forecasting for fractured reservoirs, particularly excelling at accurately predicting self-regression in late-life prediction for wells with substantial historical data. However, forecasting for new wells or those with limited historical data presents greater challenges. The main difference lies in the insufficiency of historical data, which requires meaningful integration of a continuous production data stream to enhance prediction results. In the context of utilizing real-time data and continuously updating predictions, many existing formulations of RNN-based models in the literature either utilize inputs of sub-sequences instead of the entire sequence or require retraining of the entire model to handle new data and update the predictions. Therefore, we propose a novel prediction framework called Recurrent Updated Forecasting (RUF). This framework considers the entire time series structure, improves accuracy and training efficiency, and allows for prediction updates without retraining. In scenarios without production data, we employ a Radial Basis Function Network (RBFN) to initialize the process. Once more extended production history becomes available, it can be utilized to enhance prediction results. We present a case study based on both synthetic and field data from the Montney shale gas reservoir, considering different production-related features as inputs to predict gas production rates over a three-year period. The proposed method is evaluated using the P10, P50, and P90 values of the testing Root Mean Square Error (RMSE). The results demonstrate that our method outperforms existing approaches, exhibiting outstanding efficiency, reliability, and accuracy.
Search from 250 M+ research papersSearch from 250 M+ research papers
Dec 1, 2023
P P Osipov + 1
Open Access