The Importance of Wax-Deposition Measurements in the Simulation and Design of Subsea Pipelines
- Kamran Akbarzadeh (Schlumberger) | John Ratulowski (Schlumberger) | Dmitry Eskin (Schlumberger) | Tara Davies (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Projects, Facilities & Construction
- Publication Date
- June 2010
- Document Type
- Journal Paper
- 49 - 57
- 2010. Society of Petroleum Engineers
- 4.3.1 Hydrates, 5.2.1 Phase Behavior and PVT Measurements, 4.3.3 Aspaltenes, 4.1.9 Tanks and storage systems, 5.2 Reservoir Fluid Dynamics, 5.3.2 Multiphase Flow, 4.2 Pipelines, Flowlines and Risers, 4.3 Flow Assurance, 4.3.4 Scale, 4.2.5 Offshore Pipelines
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Conventional practices for estimating the amount of deposited wax in pipelines are usually based on predictions made with simulation packages using limited stock-tank-oil (STO) deposition data collected under laminar-flow conditions in bench-scale flow loops. Such practices are conservative and often lead to nonoptimal designs of pipelines and surface facilities. For optimized designs, laboratory-scale deposition measurements made under realistic conditions are required to calibrate flowline models. In this work, a high-pressure deposition cell that operates on the Taylor-Couette (TC) flow principle is used to generate more deposition data with live reservoir fluids under turbulent flow similar to the conditions encountered in many flowlines. The analogy between TC flow and pipe flow is explained, and a scalability flow chart for linking the laboratory-scale deposition data from TC configuration to pipe configuration is presented. Through a case study, the scaled-deposition data are then used to tune a wax-deposition model in the OLGA®5 simulation package. Next, the tuned model is applied to predict wax deposition under actual production and transportation conditions. The importance of tuning the deposition models with live fluid data under turbulent-flow conditions is also shown by comparing results obtained from conventional dead-oil low-shear data.
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