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Monitoring Multiphase Flow via Earths Field Nuclear Magnetic Resonance Keelan ONeill University of Western Australia School of Mechanical and Chemical Engineering SUT Subsea Controls Down Under 20 th October 2016 Fluid Science &

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SLIDE 1

Monitoring Multiphase Flow via Earth’s Field Nuclear Magnetic Resonance

Keelan O’Neill

University of Western Australia School of Mechanical and Chemical Engineering

SUT Subsea Controls Down Under

20th October 2016 Fluid Science & Resources www.fsr.ecm.uwa.edu.au/

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SLIDE 2

Motivation for research

2

The future of oil and gas production

  • Development of deeper offshore fields

and marginal fields

  • Increased produced water
  • Increased subsea processing

Production hub Satellite field wells Subsea manifolds Satellite field wells

Atkinson, I., et al., A New Horizon in Multiphase Flow

  • Measurement. Oilfield Review, 2004. 16(4): p. 52-63

Subsea production arrangement

Objectives of flow metering

  • Measurement of phase fractions
  • Measurement of phase velocities
  • Flow regime identification
  • J. Yoder, The many phases of multiphase flowmeters,

Pipeline & Gas Journal, 240 (2013) 40-41 100 200 300 400 500 2011 2012 2013 2014 2015 2016

Total shipments ($US million) Year

World Multiphase flow meter market

14% growth

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SLIDE 3

Commercial magnetic resonance flowmeter: M-Phase 5000

Multiphase flow meter technologies

3

Advantages of nuclear magnetic resonance

  • Non-invasive measurement
  • Non-radioactive technology
  • Flow regime independent

Common measurement principles

  • Gamma ray attenuation
  • Electrical impedance measurement
  • Ultrasonic measurement systems

Superficial Gas Velocity, USG [m/s] Superficial Liquid Velocity, USL [m/s] Flow direction Upstream transducer Downstream transducer Ultrasonic emission Flow Gamma-ray source Detector Collimator

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SLIDE 4

Nuclear magnetic resonance

4

Ultra-low field Low field High field

Summary of nuclear magnetic resonance (NMR) magnetic field strengths

50 µT 50 mT 7 T Earth’s field NMR Well-logging tool Magnetic resonance imaging Chemical spectroscopy

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SLIDE 5

Nuclear magnetic resonance

5 tdelay ta 90o RF pulse Free induction decay

Pulse and Collect Experiment

Basic classical mechanics description

  • 1. Align nuclei (1H atoms) with

magnetic field (polarisation) B0 M

X Z Y

  • 3. Observe the

magnetisation relax

  • 2. Apply radio

frequency pulse

Radio frequency pulse

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SLIDE 6

The Earth’s field NMR flow meter

6 Pre-polarising magnet EFNMR detection coil Flow direction Faraday cage

Key system features

  • Detected in the Earth’s

magnetic field

  • Time of flight measurement
  • Remote detection system

EFNMR: Earth’s field nuclear magnetic resonance Independent flowrate measurement

  • 1. Polarisation

B0 M

X Z Y

  • 2. Excitation

Radio frequency pulse

  • 3. Detection
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SLIDE 7

Model for NMR signal

7

LP

𝐓(𝑤, 𝑢𝑏) = 𝑇0 1 − 𝑓

− 𝜐𝑄 𝑈

1 𝑓

− 𝜐𝑄𝐸 𝑈

1

1 − 𝑢𝑒𝑓𝑚𝑏𝑧 + 𝑢𝑏 𝜐𝐸 𝑓

− 𝑢𝑒𝑓𝑚𝑏𝑧+𝑢𝑏 𝑈

2 ∗

τ𝐸 = 𝑀𝐸 𝑤 τ𝑄𝐸 = 𝑀𝑄𝐸 𝑤 τ𝑄𝐸 = 𝑀𝑄𝐸 𝑤

Halbach array EFNMR Detection

Flow

LD

Polarisation Intermediate decay Detection

tdelay ta 90o RF pulse Free induction decay

Par arameters rs L: length τ: residence time v: velocity S0: initial magnetization T1: spin-lattice relaxation T2

*: observed spin-spin

relaxation

LPD

Pulse and Collect Sequence

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SLIDE 8

Tikhonov Regularisation

8

Real image Reconstructed image Image with noise

  • M. Bertero and P. Boccacci, Introduction to inverse problems in imaging. 1998, CRC Press.

Pipe velocity distribution r z

The inverse problem

Model transfer matrix NMR Signal Velocity probability distribution

A × P 𝑤 = S  P(𝑤) = A−1 × S

Tikhonov regularisation

  • Least squares fitting

method

  • Allows complex models to

be fit to experimental data

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SLIDE 9

Single phase velocity analysis

9

1 2 3 4 5 6 0.5 1 1.5 2

P(v) Velocity [m/s]

0.18 m/s 0.35 m/s 0.53 m/s 0.71 m/s 0.88 m/s 1.06 m/s 5 10 15 20 25 30 0.1 0.2 0.3 0.4 0.5 0.6

NMR Signal [μV] Time since pulse [s]

0.18 m/s 0.35 m/s 0.53 m/s 0.71 m/s 0.88 m/s 1.06 m/s Expt. data Model fit

Experimental NMR signals fit using regularisation Corresponding velocity probability distributions

Experimental conditions Single phase water flows at 4 – 52 L/min (0.08 – 1.15 m/s)

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SLIDE 10

Velocity comparison

10

0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2

Predicted mean velocity from NMR analysis [m/s] Measured mean velocity from in-line rotameter [m/s]

  • 5.0%
  • 2.5%

0.0% 2.5% 5.0% 0.2 0.4 0.6 0.8 1 1.2

NMR predicted velocity deviation Measured mean velocity from in-line rotameter [m/s]

Comparison of measured mean velocities Mean absolute error = 1.9 % Deviation plot

NMR measurement section Independent rotameter measurement (Uncertainty of ± 5%)

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SLIDE 11

0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2

NMR predicted velocities [m/s] Rotameter measured velocities [m/s]

Two pipe analysis

11

Experimental conditions

  • Overall flow rates:

15 – 50 L/min

  • Separation distance (LPD):

0.68, 0.78. 0.88 and 0.98 m

Velocity (pipe A) ~ 33% of velocity (pipe B) Flow

Pipe A Pipe B Halbach array EFNMR detection coil

1 2 3 4 0.4 0.8 1.2 1.6

P(v) Velocity [m/s]

30 l/min 40 l/min 50 l/min

LPD

Two pipe system velocity probability distributions Comparison of measured mean velocities

B 0.68 m 0.78 m 0.88 m 0.98 m Pipe A LPD

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SLIDE 12

Gas/liquid analysis

12

Flow regime identification

20 40 60 80 10 20 30 40 50

NMR Signal [µV] Time [s]

Stratified flow Slug flow

Video analysis

Video analysis region

EFNMR Detection Coil Halbach Array

ID: 31 mm Liquid Holdup Time [s] USL = 0.13 m/s USG = 0.88 m/s

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SLIDE 13

Velocity and holdup determination

13

2 4 6 8 0.1 0.2 0.3 0.4

NMR signal [µV] Time since pulse [s]

Experimental data Model fit 1 2 3 4 0.25 0.5 0.75 1 P(v) Velocity [m/s]

Processed signal & model fit

Velocity probability distribution

0.25 0.5 0.75 1 10 20 30 40

Velocity [m/s] Overall time [s]

𝑦𝑀 = 𝑇0,2𝑄 𝑇0,𝐺𝑣𝑚𝑚

𝑇0,2𝑄 𝑇0,𝐺𝑣𝑚𝑚

Liquid holdup determination

0.25 0.5 0.75 1 10 20 30 40

Liquid Holdup Overall time [s]

Stratified flow Slug flow

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SLIDE 14

0.00 0.25 0.50 0.75 1.00 10 20 30 40 50 60

Liquid Holdup

Video holdup NMR holdup 0.00 0.25 0.50 0.75 1.00 10 20 30 40 50 60

Liquid Holdup Experimental Time [s]

Video holdup NMR holdup

Holdup analysis comparison

14 Liquid: 4 L/min Gas: 40 L/min Liquid: 8 L/min Gas: 40 L/min Liquid: 12 L/min Gas: 20 L/min

Higher frequency slug flow Low frequency slug flow Stratified flow

0.00 0.25 0.50 0.75 1.00 10 20 30 40 50 60

Liquid Holdup

Video holdup NMR holdup

Background stratified under-prediction

  • Gas bubbles
  • Meniscus
  • Increased relaxation

Slug unit Background stratified component

Partial slug capture

  • Slug residence

time ~0.2 s

  • Scan time ~0.7 s
  • Slug not fully

captured

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SLIDE 15

0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4

NMR predicted superficial velocity [m/s] Rotameter measured superficial velocity [m/s]

10 L/min 20 L/min 40 L/min Gas Flow rate

Two phase velocity analysis

15

0.25 0.5 0.75 1 10 20 30 40

Velocity [m/s] Overall time [s]

0.25 0.5 0.75 1 10 20 30 40

Liquid Holdup Overall time [s]

𝑉𝑇𝑀 = 𝑦𝑗𝑤𝑗

𝑂 𝑗=1

𝑂

0.01 0.1 1 0.1 0.2 0.3

NMR velocity standard deviation [m/s] Rotameter measured superficial velocity [m/s] 10 L/min 20 L/min 40 L/min

Gas Flow rate

Stratified Flow Slug Flow

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SLIDE 16

Conclusions

  • Can accurately measure and model the FID

signal of a moving fluid through the flow metering system

16

  • Can determine the velocity probability

distribution of liquid moving in the system via Tikhonov regularisation

  • Able estimate the liquid velocity and holdup
  • ver time for stratified and slug flow

1 2 3 4 5 0.5 1 1.5 2

P(v) Velocity [m/s]

0.22 m/s 0.66 m/s 1.10 m/s

0.25 0.5 0.75 1 10 20 30 40 50

Velocity [m/s] Overall time [s]

Stratified flow Slug flow

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SLIDE 17

Future work

  • Analyse fluid flow in further

air/water flow regimes

17

Superficial Gas Velocity, USG [m/s] Superficial Liquid Velocity, USL [m/s]

  • Fully develop analysis techniques

to be able to interpret three phase flow (oil/gas/water)

  • Apply dynamic nuclear

polarisation for signal enhancement

Hogendoorn, J., et al., Magnetic resonance multiphase flowmeter: measuring principle and multiple test results, in Upstream Production Measurement. 2015: Houston. Gas Oil Water

  • Incorporate oil into flow

metering system

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SLIDE 18

Acknowledgements

Supervisors Michael Johns, Einar Fridjonsson and Paul Stanwix Final Year Project Students

Adeline Klotz and Jason Collis

18

Thank you for your attention! Questions?

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SLIDE 19

Tikhonov regularisation

19

The inverse problem;

Model transfer matrix NMR Signal Velocity probability distribution Pipe velocity distribution r z Residual norm Second moment of P(v)

Generalised cross validation method is used to optimise the smoothing parameter (λ)

min ( A × 𝑄 𝑤 − 𝑇 2 + λ 𝑄 𝑤

2)

A × 𝑄 𝑤 = 𝑇  𝑄(𝑤) = A−1 × 𝑇

2 4 6 8 0.5 1 1.5

P(v) Velocity [m/s] λ = 1×10-3 λ = 75 (optimal) λ = 1×104

Apply Tikhonov regularisation;

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SLIDE 20

Comparison to a theoretical model

Theoretical turbulent power law distributions 𝑉 𝑠 = 𝑊

𝑁

𝑜 + 1 𝑜 1 − 𝑠 𝑆

1 𝑜

20

2 4 6 8 0.2 0.4 0.6 0.8 1

P(v) Velocity [m/s]

n = 4 n = 5.37 n = 7 Experimental

Experimental and theoretical distributions at v = 0.44 m/s 𝑜 = 𝑔 𝑆𝑓 𝑜 0.44

𝑛 𝑡

= 5.37

(Zagarola et al. 1997)