Researchers Accurately Predict Rock Thermal Conductivity

Sunday, June 14, 2020 - 14:29

Scientists and their industry colleagues have discovered a method to accurately predict rock thermal conductivity, a crucial parameter for enhanced oil recovery.

According to the research, supported by Lukoil-Engineering LLC and 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, SciTechDaily reports.

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.

“Today, three core problems rule out any chance of measuring thermal conductivity directly within non-coring intervals. It is, firstly, the time required for measurements: petroleum engineers cannot let you put the well on hold for a long time, as it is economically unreasonable. Secondly, induced convection of drilling fluid drastically affects the results of measurements. And finally, there is the unstable shape of boreholes, which has to do with some technical aspects of measurements,” Skoltech Ph.D. student and the paper’s first author Yury Meshalkin says.

“If we look at today’s practical needs and existing solutions, I would say that our best machine learning-based result is very accurate. It is difficult to give some qualitative assessment as the situation can vary and is constrained to certain oil fields. But I believe that oil producers can use such indirect predictions of rock thermal conductivity in their EOR design,” Meshalkin notes.

Scientists believe that machine-learning algorithms are a promising framework for fast and effective predictions of rock thermal conductivity. These methods are more straightforward and robust and require no extra parameters outside common well-log data. Thus, they can “radically enhance the results of geothermal investigations, basin and petroleum system modeling and optimization of thermal EOR methods,” the paper concludes.

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