Gas Wells Production Forecast Using Artificial and Recurrent Neural Networks Models and Comparison with Traditional Decline Curve Analysis Techniques

Abstract Accurate production forecasting is crucial for effective reservoir management and field development planning. Traditional methods; such as numerical simulation, material balance equations coupled with well models, and advanced decline curve analysis (DCA) require extensive data and involve tradeoffs between accuracy and complexity. Recently, deep learning techniques, specifically artificial neural network (ANN) and recurrent neural network (RNN) models have demonstrated promising performance in this domain. This study applies ANN and RNN for production forecasting and compares their effectiveness with traditional DCA models. An actual production dataset for a gas field containing 13 wells is used to compare ANN and RNN models to traditional decline curve analysis models. After thorough data cleaning and preparation, the dataset was divided into training (historical) and test data sets. The training data was used to build ANN and RNN models in addition to estimating the model parameters for traditional DCA models. Traditional DCA models that were investigated include Arps (exponential, hyperbolic, and harmonic), Duong, Power Law Exponential Decline (PLE), and Stretched Exponential Decline (SEPD). Finally, the prediction of different models was evaluated and compared using the testing dataset. The comparison also included long, medium, and short-term prediction. Based on the comparison of daily gas production rate forecast, the ANN model demonstrated notable improvements over Arps models for the majority of wells, with a mean absolute percent error (MAPE) of 3.9% compared to 9.1%. However, across all wells, the average MAPE was slightly lower at 6.3% versus 8.1%. Conversely, the RNN model exhibited varied performance compared to Arps models. It showed poorer long-term performance (13% versus 4.5%), similar performance in the medium term (4.4% versus 4.2%), and superior performance in the short term (2.7% versus 4.2%). Furthermore, among DCA models specifically developed for unconventional wells, SEPD exhibited nearly identical performance to Arps (8.3% compared to 8.1%). In contrast, Duong model demonstrated the poorest performance with MAPE of 21.9%. This study provides insights into model analysis and introduces guidelines in using artificial and recurrent neural networks for predicting production rates. The ANN and RNN models were developed through careful testing with different designs and techniques. All the models in this research were created using Python scripts and powerful open-source libraries like SciPy and TensorFlow.