Machine Learning for Real-Time Hydrate Risk

Andrew Yule, Pontem Analytics

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ABSTRACT

This talk will discuss how the advanced neural network framework within the Wolfram Language was used to train and develop a model built from simulation-based data that went on to be used for real-time monitoring of hydrate formation risk at an LNG (liquified natural gas) facility.

Pontem Analytics

www.pontemanalytics.com

Specialist consulting company focused on combining engineering and data-driven analysis

Our Verticals

What are hydrates?

Clathrate compound in which water molecules form a solid, ice-like lattice structure that en-cages gas molecules, typically methane

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Stable at high pressures and low temperatures → unlike ice, they form well above freezing temperatures

Liquified natural gas (LNG)

Natural gas (methane) that has been converted into a liquid form for ease of storage and transportation

The process of liquefying natural gas involves cooling it to extremely low temperatures, typically around -260 °F (-162 °C ), at which point it becomes a cryogenic liquid

Injection of ethane → increases the heating value, more easily transported as a liquid

The injection of liquid ethane when mixed with the natural gas will cause vaporization of the ethane leading to cooling

Cooling will lead to a hydrate risk depending on how much water we have in the methane and ethane streams

8 - 12 °F is a reasonable margin to target

Physics-based modeling

Actual drawings of injection equipment

Computational fluid dynamics

Process modeling

Parametric study

What the project needed

Defined the risk → hydrates

Defined the ideal margin to stay above → 8-12°F

Created detailed CFD models to simulate the expected behavior

Created a faster process simulation based model to more easily perform simulations with various sensitivities

“We would like to be able to monitor our hydrate formation margin in real-time to alert operations whether they need to cut back ethane injection or stop it altogether”

Wolfram’s Advantage

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Deployment

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Recapping

Years worth of historical data were analyzed with Wolfram Language (not discussed)

Easy screening of various machine learning models was able to be performed by Wolfram Language to assist with the type of model ultimately needed

The neural network framework within the language was used to build and train a model that achieved acceptable accuracies

Scripts were easily setup to train additional models (mixture temperature, not discussed)

Symbolic architecture of the language made it easy to extract key information from the neural network so that the model could be deployed in the easiest manner possible