Abstract
Hydrate is one of the most common challenges in flow assurance. Mechanism model or empirical model is usually adopted to predict hydrate formation under a specific condition. However, the methods are difficult to operate in real-time change of actual situation. In this paper, a mechanism-based data-driven modeling method is built to predict hydrate formation. Based on the collected data, including temperature, pressure and components, a data-driven method is introduced to identify the unknown parameters in the mechanism model. 131 groups of experimental data were collected to make a correlation analysis to determine the main components affecting hydrate formation. Four different component systems were calculated using the mechanism model (P-P model), empirical model (Makogon model) and data-driven mechanism model for comparison. Results show that the average error of the data-driven model is as low as 0.0085 MPa, and this method can overcome the irrationality of prediction caused by only using historical data or mathematical formula.
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