Application of machine learning to predict the time evolution of condensate to gas ratio for planning and management of gas - condensate fields
Abstract
One of the most important parameters for evaluating, forecasting, and managing gas - condensate fields is the evolution of the condensate to gas ratio (CGR) over time. This parameter tends to decrease as reservoir pressure declines. Conventionally, gas and condensate samples are collected initially at the time starting production and periodically later to conduct laboratory analyses of fluid composition, properties and CGR. However, sampling, transporting and analysing samples take time and effort and, therefore, could be very expensive. To predict CGR over time, likewise, dynamic models are also frequently used. However, these models could include many uncertainties due to the assumption of input data, including reservoir structures, fluid phase interaction, and reservoir property distribution. Therefore, application of machine learning to predict the time evolution of CGR in this research is a new and effective approach to supplement conventional methods.
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