Management Strategies in Subsea Oil and Gas Flowlines Young Persons - - PowerPoint PPT Presentation

Towards Risk-Based Hydrate Management Strategies in Subsea Oil and Gas Flowlines

Young Persons’ Lecture Competition IOM3 Western Australia Vincent Lim, PhD Candidate The University of Western Australia 25th May 2018

Motivation: Increased Energy Demand in Future

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• Global energy demand is predicted to almost double in

2040 compared to 2000

• Gas contributes a quarter of the total energy production

Source: International Agency, 2018

Liquified Natural Gas: Alternative to Use of Diesel in Local Mining Industry

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Liquified Natural Gas (LNG)

• Predominantly methane and some ethane
• Joint venture by local petroleum promote

LNG over diesel in mining industries

• Cleanest-burning fossil fuel
• Less environmental hazard

1* Hussein, A., Oil Industry Insight, 2018 2* Government of WA, Department of Mines and Petroleum, North West Shelf Oil & Gas, 2015

• Natural gas resources are

plentiful in Western Australia

• Q: What are the challenges for

gas production in pipelines?

*1 *2

Hydrates!

What are Gas Hydrates?

4 Water molecules Methane molecule

Gas hydrates

• Ice-like solid compounds
• Small gas molecules (e.g. CH4) trapped within hydrogen bonded water cage
• Stable at high pressure and low temperature

Hydrate stability region Hydrate- free region

Pipeline: Hydrate plug (Image from Petrobras)

$ 1M lost per day if pipeline plugging

• ccurs!

Experiment: Hydrate crystal formed in Autoclave

Natural Gas Hydrate Formation is Stochastic (aka random)

• 4 measurements of hydrate formation in field
• Minimum subcooling of 6.5 ˚F (≈ 3.6 K)
• Used in hydrate simulation software: OLGA

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*1 Matthew et. al., Annals of the New York Academy of Sciences, 2000

Field test: Werner Bolley well, Southern Wyoming Q: Is this 3.6 K universally applicable to oil and gas pipelines? Q: Will water be immediately converted to ice at 0 °C? Hydrate-free region Hydrate stability region ΔT1 Formation ΔT2

Minimum subcooling Hydrate equilibrium curve Pipeline

*1

HPS-ALTA is more Efficient in Generating Hydrate Formation Event

High Pressure Stirred Automated Lag Time Apparatus

• Stirred system (to remove mass transfer limitation)
• Peltier driven cooling system (2 K/min)
• Able to generate large number of experimental runs

(typically on the order of 100 data points)

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rotate

0 rpm 700 rpm Traditional Apparatus: Autoclave (~2 K/hr) Data are not statistically significant

Methodology: Hydrate Formation Detection by Pressure Drop

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Initial condition Hydrate formation induced gas consumption Hydrate dissociation Note: 100 formation data points collected for each experimental condition

ΔT

Hydrate Formation Depends Critically on System Shear

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During pipeline shutdown (0 rpm)

• No pressure drop (formation) was observed in static system

For 100 rpm

• Probability distribution (PDF) is generated from > 100 hydrate formation events
• Cumulative distribution (CDF) generated by numerically integration

Mean 0 rpm 100 rpm

700 rpm is more representative of hydrate formation during production

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Mean

*1

*1 Lim et. al., OTCA, 2018

Increasing shear rate from 100 rpm to 700 rpm decreased mean subcooling by approximately 3 K

Note: Low subcooling indicates “easier” formation

700 rpm 100 rpm

Hydrate Prevention by Chemical Injection: KHIs is More Economical than THIs

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Key Points

• Delay formation
• Typically 0.5 to 2 wt% added, reduce

logistic costs and OPEX

• Mechanism of KHIs to delay hydrate

formation is unclear

• Will you risk using KHI?

Into the millennium: Kinetic Hydrate Inhibitors (KHIs)

Key Points

• Methanol / glycol
• Completely prevent hydrate formation
• Uneconomical if high dosage

(typically 50 wt%) is required Traditional solution: Thermodynamic Hydrate Inhibitors (THIs)

KHI Suppress Nucleation, Less Stochastic Distribution Obtained

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Key Points

• Mean subcooling obtained with 1 wt% KHI increased 3.3 K
• Standard deviation of distribution with KHI decreased by 3 to 5 times

Increase KHI concentration

With large formation datasets, we can fit experimental datasets to hydrate formation theory. Modelling cannot be done with data collected with conventional apparatus with repeatability of 5 points!

*1 May et. al., Langmuir, 34, 10, 3186-3196 2018

*1

Conventional apparatus

Conclusion and Way Forward

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Summary 1) Increased risk of hydrate formation as natural gas production moves towards deeper subsea region 2) HPS-ALTA is efficient in generating statistically significant datasets to study hydrate formation 3) Detailed characterization of KHI performance 4) Hydrate formation distribution data can be fitted with hydrate nucleation theory Way Forward

• Rank performance of different inhibitors
• Perform modelling work on hydrate formation data
• Promote hydrate formation model to industry (e.g. OLGA)

ACKNOWLEDGEMENTS

Eric May Mike Johns Zachary Aman Peter Metaxas Paul Stanwix

THANK YOU QUESTIONS?

Comparisons of Conventional Apparatus to Screen Hydrate Formation

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*1 *2

*1 High pressure autoclave owned by UWA *2 Maeda et. al., Review of Scientific Instruments, 82, 065109, 2011

• Direct P & T measurements
• Visual observation
• Cooling rate: ~1 K/hr
• ~10 hours per run
• Formation data are not

statistically significant Autoclave: High pressure stirred cell with impeller High Pressure Automated Lag Time Apparatus (HP- ALTA): Mini high pressure cell

• Sudden decrease in transmitted light intensity when

solids form

• Large range of cooling rate: up to 5 K/min
• Can generate lots of formation data
• Only work in static system
• Only work for transparent sample

Methodology: Hydrate Formation Detection by Pressure Drop

Subcooling 15 Python automation

Experimental Methodology a) Constant cooling rate b) Hydrate forms indicated by pressure drop c) Hold temperature at Tlow for 5 minutes d) Heat the cells to regeneration temperature a b c d

Formation T Equilibrium T

Statistical Data for KHI Experiments

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