Artificial Neural Networks for Optimization of Natural Gas Flow Through Surface Well Chokes

Abstract Optimizing natural gas flow through surface well chokes is critical for maximizing production efficiency and ensuring reservoir integrity. Traditional methods, such as empirical correlations and mechanistic models, often struggle to accurately predict flow rates due to the nonlinear and dynamic nature of gas flow. This paper explores the application of artificial neural networks (ANNs) as a data-driven approach to optimize gas flow through surface well chokes, empirical equation was also developed based on the optimized ANNs model. The ANN model was trained using a comprehensive dataset and then validated against unseen data. The results demonstrate that the ANNs model accurately predicted the gas flow rate for the training data. Additionally, the trained ANNs model was used to derive a predictive equation that can be applied in real-time operations, providing accurate and reliable recommendations for choke settings. The gas flowrate was predicted for the validation data set using the developed equation with a high accuracy, the average absolute percentage error was only 3.77% and the room mean square error was 0.28 MMscf/day. This extracted equation, derived from the ANN model, offers a practical tool for field engineers, enabling them to make informed decisions to optimize natural gas flow. This study highlights the potential of ANNs to enhance the optimization of natural gas production processes, offering a robust alternative to traditional methods. The findings suggest that integrating ANNs-based model and equation into well management practices can lead to significant improvements in operational efficiency and economic outcomes, marking a step forward in the digital transformation of the oil and gas industry.