In this work , we use a model-based flow assurance computational framework developed previously [2] for the identification of optimal hydrate prevention policies during transient operation. A multi-phase flow model (based on the drift-flux model [3,4]) that describes the transient thermal-hydraulic behaviour of sub-sea pipelines is coupled with a hydrate thermodynamic equilibrium calculation module [5]. This model-based approach allows us to determine whether hydrate formation can occur at any time instant and at any point along the pipe network by comparing the hydrate formation equilibrium temperature and the actual fluid temperature inside the pipe. The injection of thermodynamic hydrate formation inhibitors such as methanol is also included in the model. The framework is implemented in a state-of-the-art dynamic modeling tool (gPROMS®) [6], which allows the easy modular construction of pipe networks with different geometries and the use of a range of models for heat transfer coefficients or drift-flux correlations. In order to demonstrate the capabilities of the proposed framework, we consider a case study based on typical conditions typical in the Gulf of Mexico. We obtain optimal control policies (optimal inhibitor dosages and depreessurisation rates) that ensure hydrate-free operation under a transient scenario in which the pipeline is shut-in at the choke valve (topsides) for 10 hours and then restarted. We investigate the impact of different objective functions, such as the minimisation of lost production or the minimisation of methanol injection costs, on the operating policy. The re-opening rate of the choke valve is a very sensitive parameter due to rapid cooling (Joule-Thomson effect) which may lead to hydrate formation. The control strategies identified via dynamic optimisation take into account the trade-offs that exist between a rapid restart and a hydrate-free restart. Our studies demonstrate the potential benefits of a model-based approach in dealing with the complex dynamics of the flow assurance problem.
[1] Macintosh, N., 2000, Offshore, 60(10).
[2] Luna-Ortiz, E., Lawrence, P, Pantelides, C. C, Adjiman, C. S. and Immanuel, C. D. (2008), 18th European Symposium on Computer Aided Process Engineering – ESCAPE 18
[3] Ishii, M. and Hibiki, T., 2006, Thermo-Fluid Dynamics of Two-phase Flow, Springer
[4] Shi, H., Holmes, J., Durlofsky, L., Aziz, K., Diaz, L., Alkaya, B. and Oddie, G., 2005, SPE J., March, 24-33.
[5] van der Waals, J. H. and Platteeuw, J. C., 1959, Adv. Chem. Phys., 2, 2-57.
[6] PSE, 2007, gPROMS® ModelBuilder 3.0.3, www.psenterprise.com