Emissions Reduction Visual Presentation R02: Application of data analytics models to support LNG plant energy efficiency improvements
Matthew Ladner A * and Qi Chu A *A
Matthew Ladner joined Woodside in 2010 and has over 18 years of upstream and LNG industry experience, including process engineering design, integrated production forecasting, and 12 years of frontline process engineering support on LNG plants. He graduated with a Bachelor of Chemical Engineering from Curtin University in 2006 and is currently responsible for developing integrated energy efficiency and emissions reduction management systems for the Karratha Gas Plant. |
Qi Chu is a physicist and data scientist with over 13 years of experience in physics modelling and computational sciences. She combines her PhD in Physics with expertise in Computer Science and Engineering. Qi has been with Woodside for over 3 years, focussing on developing simulation solutions and analytics modelling products for production optimisation at Pluto and Karratha gas plants. |
Abstract
Emissions Reduction Visual Presentation R02
The Woodside-operated Karratha Gas Plant (KGP) is a large-scale integrated gas production system located in Karratha, Western Australia. Producing liquefied natural gas (LNG), domestic gas, condensate and liquefied petroleum gas (LPG) through five LNG processing trains; two domestic gas trains; six condensate stabilisation units and three LPG fractionation units. Woodside actively pursues opportunities to reduce greenhouse gas (GHG) emissions in operations, including the use of data analytics techniques to inform our operations teams on plant energy efficiency optimisation. Two examples of data analytics application in LNG plant energy efficiency optimisation are presented: (1) power generation config explorer – an analytical and logic solver model with ‘now-casting’ capability and (2) a live plant-wide energy efficiency metric with built-in thermodynamic calculation functionality. The power generation config explorer tool is an advisory application which provides a recommended operation config (number of generators and type) to meet operational constraints, maximise energy efficiency and reduce GHG emissions. The tool uses machine learning techniques to overcome the challenge of predicting reactive power in a complex alternating current (AC) power network and a logic solver to mimic advanced process control behaviour. Core to energy management is accurate measurement of energy consumption and energy production. The conversion of LNG product ‘in-tank’ to an energy equivalent basis is a common challenge due to the need to correct for boil-off gas losses. A data analytics approach has been applied using live plant data integrated with thermodynamic equation of state calculation and numerical optimisation methods to account for heat losses and other uncertainties.
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Keywords: data analytics, data science, data visualisation, energy efficiency, GHG emissions reduction in operations, LNG production, process simulation, turndown.
Matthew Ladner joined Woodside in 2010 and has over 18 years of upstream and LNG industry experience, including process engineering design, integrated production forecasting, and 12 years of frontline process engineering support on LNG plants. He graduated with a Bachelor of Chemical Engineering from Curtin University in 2006 and is currently responsible for developing integrated energy efficiency and emissions reduction management systems for the Karratha Gas Plant. |
Qi Chu is a physicist and data scientist with over 13 years of experience in physics modelling and computational sciences. She combines her PhD in Physics with expertise in Computer Science and Engineering. Qi has been with Woodside for over 3 years, focussing on developing simulation solutions and analytics modelling products for production optimisation at Pluto and Karratha gas plants. |
