Skoltech scientists and their industry colleagues have found a way to use machine learning to accurately predict rock thermal conductivity, a crucial parameter for enhanced oil recovery.
The research, supported by Lukoil-Engineering LLC, was published in the Geophysical Journal International.
Rock thermal conductivity, or its ability to conduct heat, is key to both modeling a petroleum basin and designing enhanced oil recovery (EOR) methods, the so-called tertiary recovery that allows an oil field operator to extract significantly more crude oil than using basic methods. A common EOR method is thermal injection, where oil in the formation is heated by various means such as steam, and this method requires extensive knowledge of heat transfer processes within a reservoir.
For this, one would need to measure rock thermal conductivity directly in situ, but this has turned out to be a daunting task that has not yet produced satisfactory results usable in practice. So scientists and practitioners turned to indirect methods, which infer rock thermal conductivity from well-logging data that provides a high-resolution picture of vertical variations in rock physical properties.
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