Exhibition & Conference

13-16 September 2021

Singapore EXPO, Singapore

Technical Programme

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Andres Bustillo

Operations Coordinator

SPEC LNG


15:00 - 15:30

Wednesday, 18 September 2019

T1.5 Scenario-driven Modeling and Forecasting of BOG Production and LNG Aging

Scenario-driven Modeling and Forecasting of Boil-off gas Production and LNG Aging in Floating Storage and Regasification Units under Intermittent Dispatching Operating Conditions. Boil-off gas (BOG) is an inherent attribute in LNG storage facilities, and its magnitude has a significant impact in the Terminal´s economic performance.  In Floating Storage and Regasification Units (FSRU) there are different BOG pathways, including internal consumption for power and steam generation, recondensation using LNG at the regasification train, and combustion at the Gas Combustion Unit (GCU) of excess BOG.  Most FSRUs manufacturers provide a table of expected BOG production under different regasification flows and heat transfer rate for every storage tank.  A challenging condition occurs when FSRU facilities are used as a backup source for natural gas demand in the country, leading to intermittent operation during most of the year.  Under these conditions, data has shown deviation of BOG production in excess of 5% compared to information provided by the FSRU manufacturer, mostly due to variables and operating conditions not considered in the provided estimates.  Additionally, when no regasification takes place over long periods of time, BOG leads to significant changes in the composition of the stored LNG, which cannot be easily measured since chromatography equipment is traditionally installed in the regasified outlet stream.  Such changes in composition may even lead to stored LNG not meeting regulatory requirements. 

In this paper, a modeling and forecasting strategy for both BOG production rates and stored LNG aging is presented.  These models were developed based on the experience and data from the FSRU operating at SPEC LNG terminal in Colombia, and they have allowed early detection of undesired operating conditions, optimization of operation and dispatching patterns, and further enrichment of the models based on new variables whose correlation with modeled outputs has been identified. 

The computer-based models to be presented have experienced two stages of development, and validation of their results will also be presented, while comparing their accuracy and usability with respect to traditional manufacturer’s supplied predictions and operational information available.