Europe User Conference: Modelling-waxy-fluids
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This document summarizes a presentation on modelling waxy fluids in pipelines. It discusses wax deposition risks like reduced flow and increased pressure drop. Key steps in the modelling process are fluid characterization, tuning the wax appearance temperatures and content to match lab data, and running steady-state simulations. A case study applies these methods to an existing North Sea pipeline, tuning the model to benchmark against operational data and predict deposition in a new export route.
Europe User Conference: Modelling-waxy-fluids
- 1. woodplc.com Modelling Waxy Fluids in Multiflash Mehwish Manzoor and Jessy Zeng 18th June 2018
- 2. • Wax Management / Control • Wax Deposition and Risks • Modelling Waxy Fluids in Multiflash • Case Study – Fluid Characterisation – Wax Content Tuning – WAT and WDT Tuning – Benchmarking • Conclusion Agenda 2 A presentation by Wood.
- 3. Wax Management / Control 3
- 4. General Approach to Control of Wax Issues Wax Management / Control 4 A presentation by Wood. Pigging < Insulation Chemical Operational > Insulation Heating RISK CAPEX / OPEX
- 5. Wax Deposition and Risks 5
- 6. • High molecular weight paraffins (typically n-C17+) Wax in Fluids 6 A presentation by Wood. Higher Molecular Weight Quickest Precipitation
- 7. • Molecular Diffusion is the dominant wax deposition mechanism • Radial diffusion of dissolved wax molecules in the oil • Concentration gradient between dissolved wax in the turbulent core and the wax in solution at the pipe wall • Dissolved wax diffuses towards the wall where it precipitates Wax Deposition (Molecular Diffusion) 7 A presentation by Wood.
- 8. Wax Deposition Risks 8 A presentation by Wood. Reduction in Delivery • Reduction in flow area • Increased Pressure Drop • Change in wall friction • Change in fluid viscosity Pipeline Integrity • Trapped H2O may corrode pipeline Remediation Required • Loss of production • Risk of “stuck” pigs – long shutdowns • Additional topsides equipment required (i.e. chemicals plus processing)
- 9. Wax Deposition Risks 9 A presentation by Wood. Reduction in Delivery • Reduction in flow area • Increased Pressure Drop • Change in wall friction • Change in fluid viscosity Pipeline Integrity • Trapped H2O may corrode pipeline Remediation Required • Loss of production • Risk of “stuck” pigs – long shutdowns • Additional topsides equipment required (i.e. chemicals plus processing) Fluid characterisation and tuning against lab data is crucial to accurately predict the likely wax deposition in field operation.
- 10. Modelling Waxy Fluids in Multiflash 10
- 11. Wax Model in Multiflash 11 A presentation by Wood. Coutinho Wax Model Solid wax phase treated as non-ideal mixture of n-paraffins Equation of State RKSA / CPA-InfoChem Minimal difference in prediction of wax phase behaviour in crude oils Viscosity Default - SuperTRAPP Oil Systems – Pedersen (Heavy Oils) is good
- 12. Input Data Required for Modelling Waxy Fluids 12 A presentation by Wood. Compositional Analysis Data • Fluid Composition • Calculated Sample Properties (i.e. MW and Density) N-Paraffin Distribution • HTGC • Wax Content Wax Appearance Temperature
- 13. Additional Data for Modelling Waxy Fluids 13 A presentation by Wood. Wax Dissolution Temperature Saturates, Aromatics, Resins and Asphaltenes Analysis
- 14. PVT Report Density, Sat. Point, GOR, Viscosity Tuned Thermodynamic Fluid File Steady State OLGA Simulation Wax Deposition Modelling Procedure 14 A presentation by Wood. N-Paraffin Distribution Wax Content, WAT, WDT, Viscosity Tuned N-Paraffin Fluid File Wax Deposition OLGA Simulation
- 15. Case Study North Sea Wax Deposition Study 15
- 16. North Sea Wax Deposition Study Identification of a Suitable Wax Management Strategy 16 A presentation by Wood. Scope Benchmarking of Existing Pipeline Future Operation of New Oil Export Route Platform 3 Benchmarking Case Future Operation Platform 2Platform 1
- 17. Fluid Characterisation 17 A presentation by Wood. Real ComponentsIso-ParaffinsN-Paraffins
- 18. • Lab Data = 5.0 wt% • N-Alkane Distribution (n-C20+) = 3.99 wt% • Predicted Wax Content @ -30 °C = 4.05 wt% Wax Content Tuning 18 A presentation by Wood.
- 19. WAT and WDT Tuning Approach 19 A presentation by Wood. Lab Test Methods Cross Polar Microscopy Differential Scanning Calorimetry WAT and WDT Tuning Client Request Less Conservative WAT Tuning Only Preferred Method Allows Prediction of WDT
- 20. Fluid Laboratory Results Model Prediction WAT (°C) WDT (°C) WAT (°C) WDT (°C) 1 36.9 53.3 36.4 53.7 2 38.7 48.9 38.4 49.3 WAT and WDT Tuning 20 A presentation by Wood.
- 21. 40.7 7.3 40.7 7.3 0 5 10 15 20 25 30 35 40 45 Export Temperature Arrival Temperature Temperature(°C) Benchmarking - Temperature Operational Data Simulation Result Benchmarking – Pressure and Temperature 21 A presentation by Wood. 22.5 18.76 22.4 18.76 16 17 18 19 20 21 22 23 Export Pressure Arrival Pressure Pressure(barg) Benchmarking - Pressure Operational Data Simulation Result • Steady state wax deposition simulation in OLGA; • Wax content under-predicted in Multiflash – DCM tuned to match pressure drop;
- 22. Conclusion 22 A presentation by Wood. Wax Modelling in Multiflash is Reliable Depending on: Availability of Fluid Data Availability of Wax Data Accuracy of Lab Measurement Techniques Tuning Approach Questions?
- 23. woodplc.com