opportunities in subsea dehydrationdata and modelling for improved design and operations

presented by Francois Kruger, supervisors: Nicolas von Solms & Georgios M. Kontogeorgis

references:1. Kruger, F.J., Kontogeorgis, G. M & von Solms, N. New association schemes for mono-ethylene glycol: Cubic-Plus-Association parameterization and uncertainty analysis. Fluid Phase Equilibria 458, 211–233 (2018).2. Kontogeorgis, G. M., V. Yakoumis, I., Meijer, H., Hendriks, E. & Moorwood, T. Multicomponent phase equilibrium calculations for water–methanol–alkane mixtures. Fluid Phase Equilibria 158–160, 201–209 (1999).3. Bjørner, M. G., Sin, G. & Kontogeorgis, G. M. Uncertainty analysis of the CPA and a quadrupolar CPA equation of state – With emphasis on CO2. Fluid Phase Equilibria 414, 29–47 (2016).4. Kruger, F.J., Danielsen, M.V., Kontogeorgis, G. M, Solbraa, E. & von Solms, N. Ternary Vapor−Liquid Equilibrium Measurements and Modeling of Ethylene Glycol (1) + Water (2) + Methane (3) Systems at 6 and 12.5 MPa. J. Chem. Eng. Data 63, 1789–1796 (2018).5. Kruger, F.J., Kontogeorgis, G. M, Solbraa, E. & von Solms, N. Multi-component vapor-liquid equilibrium measurement and modeling of ethylene glycol, water and natural gas mixtures at 6 and 12.5 MPa. Submitted for publication: J. Chem. Eng. Data (2018).6. graphic taken from https://www.equinor.com/en/magazine/the-final-frontier.html

acknowledgement the authors wish to thank Equinor for their permission to publish experimental data and financial support of this research - part of the CHIGP (Chemical in Gas Processing) project

project background• collaboration between KT-CERE and Equinor (formerly Statoil)

• evaluation of high-pressure subsea natural gas dehydration

• critical specifications:

• H2O dew point

• glycol in the gas phase

figure 1: planned workflow for the subsea processing project

does it matter?potential gains waiting to be unlocked

on-going experimental work at DTU #

• troubleshooting & re-commissioning

• installation of a new Agilent 7890B GC

• development of experimental method

• verification/validation of apparatus

• preliminary work with H2O + CH4

photo 1: three-phaseequilibrium cell located atthe CERE laboratory inLyngby – the red ROLSIsampling valves featureprominently

a fruitful partnership friends from the north#

• 6-month external research stay at Equinor in Trondheim, Norway

• state-of-the-art experimental phase equilibrium equipment: quantification of all components in all phases

starting simple MEG + H2O + CH4(4)

• experimental uncertainty ± 2-12%

• modelling with CPA yields errors between 5-20%

• very satisfactory results

adding complexity MEG + H2O + natural gas(5)

• addition of natural gas: 4% C2H6, 2.5% CO2 and trace components quantified to n-hexane (6 ppm)

• significantly larger modelling errors: 4-70% - can we improve?

• introduction of CO2 creates several modelling difficulties, especially for MEG in the gas phase

• MEG-hydrocarbon kij required to predict dissolved natural gas accurately

getting the thermodynamics right # #

new association schemes for MEG• three new association schemes proposed for MEG(1)

• CPA(2) parameter sets fitted to pure component experimental and liquid-liquid

equilibrium (LLE) data

• promising results (Figure 3) using the newly proposed 4F association scheme

• significantly smaller binary interaction parameters (kij)

finding (some) certainty in uncertainty (analysis)• bootstrap method:(3) parameter distributions indicate multiple optima

• highlighted value in use raw experimental data for parameter regression

figure 2: new associationschemes which have beenproposed and tested forMEG and make use of abipolar association site:these sites can act aselectron donors oracceptors and have beenused successfully in thedescription of 1-alkanols

1,2 ethanediolMono-ethylene glycol

Positive site Negative siteBipolar site

C C OO

4C-scheme

H H

C C OO

4E-scheme

H H

C C OO

4F-scheme

H H

C C OO

3C-scheme

H H

1% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24%

% AARDP

Sat*

*

Tx (MEG - H2

O)

Ty (MEG - H2

O)

Tx (MEG - CH4

)

Ty (MEG - CH4

)

LLE (MEG - nC6

)*

LLE (MEG - nC7

)*

Lit - Derawi (4C)

This work (4F)

95% Conf. Int.

figure 3: radar plotcomparing the relativeperformance of thenew 4F associationscheme for MEGversus the literature4C parameter set

* data included in parameter regression

49.8 50 50.2 50.4

0.99

1

1.01

1.02

a0

vs b0

All points This work 95% Confidence Interval

49.8 50 50.2 50.4

2330

2340

2350

2360

/R vs b0

49.8 50 50.2 50.4

11.7

11.8

11.9

12

12.1

10 3 vs b0 figure 4: selected co-

parameter correlation plotsand confidence intervals forMEG modelled with the new4F association scheme

figure 5: experimental results for H2O in gas forternary MEG + H2O + CH4 data, with 90 wt% MEGat T = 288-323 K and P = 60 and 125 bar.modelling performed using literature 4C parameterset for MEG, yielding an AARD of 5.2%.

285 290 295 300 305 310 315 320 325 330

Temperature [K]

0

100

200

300

400

500

600

700

800

900

ppm

[m

ol]

y2

CPA (60 bar)

y2

Experimental (60 bar)

y2

CPA (125 bar)

y2

Experimental (125 bar)

10 20 30 40

Temperature [°C]

0

5

10

15

20

yM

EG

[ppm

]

Exp (60 bar)

Exp (125 bar)

CPA (60 bar)

CPA (125 bar)

10 20 30 40 50 60

Temperature [°C]

0

200

400

600

800

1000

yH

2O

[ppm

]

10 20 30 40 50 60

Temperature [°C]

4000

6000

8000

10000

12000

xN

G [m

ol/m

ol]

10 20 30 40 50 60

Temperature [°C]

4000

5000

6000

7000

8000

9000

xC

1 [p

pm]

10 20 30 40 50 60

Temperature [°C]

350

400

450

500

550

600

650

xC

2 [p

pm]

10 20 30 40 50 60

Temperature [°C]

500

1000

1500

2000

2500

xC

O2

[ppm

]

figure 6 (top): experimental results(left to right) for MEG in gas (70%),H2O in gas (24%) and total dissolvednatural gas

figure 6 (bottom): experimentalresults (left to right) for dissolvedmethane (5%), ethane (6%) andcarbon dioxide (14%)

%AARD given in brackets

table 1: lessons learnt from experimental data (arrow: temperature trend, relative colour: relative performance subsea vs onshore)

figure 7: combined uncertainty and sensitivity studies with Monte

Carlo approach

• real-world applications e.g. The Subsea FactoryTM (graphic 1)

• decreased pressure losses (single phase flow)

• improved operability

• recovery in marginal/isolated fields (increased tieback)

graphic 1: The Subsea FactoryTM (6)

decision-making in operations and design # # #

• where can we improve current operations?

• which design opportunities can be exploited?

• opportunities with high-pressure operation

a near future: building process simulations• process simulation in Aspen Plus

• find optimal process conditions, chemical req.

• from operating points to operating windows

• evaluate performance at confidence intervals

• determine sensitivity to process upsets

• Monte Carlo simulations in Matlab

• process feasibility studies

• probability-based production forecasting

• probability-based optimization

• equipment sizing

• economic analyses

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