Abstract
To maximize production efficiency, natural gas flow via surface well chokes must be optimized. The nonlinear character of this flow frequently causes problems for conventional empirical correlations and mechanistic models. To accurately forecast gas flow rates using field data, this study develops an Artificial Neural Network (ANN) model that considers temperature, gas gravity, choke size, and pressures. The main innovation is that the optimized network is used to derive an exact, closed-form empirical equation, going beyond the typical "black-box" use of ANN. This equation enables the estimation of flow rate in real-time without requiring the execution of the ANN model, providing engineers with a valuable tool at present. The 5-neuron optimized ANN demonstrated remarkable accuracy, with training and testing average absolute percentage errors (AAPE) below 2% and a correlation coefficient (R) over 0.99. When tested on unknown data, the resultant equation performed well (R=0.999, AAPE=2.78%), outperforming conventional techniques in terms of generalization and predictive power. By connecting data-driven analytics with field operational realities for well management, this research represents a significant leap forward.
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