A unique experimental setup that allowed non-destructive high-precision laser measurements of the thermal properties of rocks. The high-performance apparatus works on rock samples of various sizes and properties varying over a wide range of values. Credit: Evgeny Popov et al./IOP Conference Series: Earth and Environmental Science
Skoltech researchers used their unique experimental data and expertise in rock modeling to test a theoretical model of rock thermal conductivity, which is commonly used in prospecting, exploration and development of oil, gas and geothermal fields, and in the disposal of radioactive waste. The study, which describes a way to significantly improve the model, is published in the journal Geothermal.
Rocks are difficult to study and model because of their varied composition and fabric (structural features), which reflect the conditions of their formation over millions of years. Their properties are much more difficult to study than those of industrial materials because rocks are heterogeneous and uniquely structured in nature. The situation is further complicated by the scarcity of rock samples for laboratory experiments, so it is not surprising that scientists must be content with theoretical modeling using approximations that greatly simplify both the structure and properties of rocks. This results in poor input data quality and low reliability of modeling tools used in the oil and gas and geothermal energy industries, radioactive waste disposal, and by other professionals who rely on insights into the subsurface thermal regime.
Current approaches for predicting the thermal conductivity of rocks are based on simplified theoretical models that do not do justice to the variety of factors that determine this rock property. As a result, thermal conductivity estimates can differ by hundreds of percent between different theoretical models. In his paper published in Geothermalthe Skoltech researchers and their colleagues argue that the most popular model of thermal conductivity is quite unreliable, especially when it comes to porous rocks.
“We tested the effectiveness of the most popular theoretical model of rock thermal conductivity and found large errors in its results, which prompted us to describe a way to improve thermal conductivity modeling,” says paper co-author and Skoltech professor Yuri Popov. “Our study is based on Skoltech’s extensive theoretical experience and a large-scale experiment involving non-destructive high-precision measurements of the thermophysical properties of rocks. We used our unique in-house equipment on a huge array of samples, examining up to 1,750 rocks from different regions – the most valuable collection of natural material we have received from Skoltech’s industrial partners thanks to our close collaboration.”
Errors in the previous model of porous rock thermal conductivity stem from the oversimplification of the rock as a medium, as the model ignores the structural characteristics of the rock and considers only the thermal conductivities of its components and their volume fractions. In part, this happened because obtaining reliable experimental data on the thermal conductivity of sedimentary rocks to test the model remained an intractable problem until recently. Earlier data on the thermal conductivity of rocks, obtained by conventional techniques and cited in existing reference books and databases, in most cases require a complete revision. The lack of strict quality control of experimental data on thermal conductivity has led to a misunderstanding of the ranges of spatiotemporal variations of this characteristic, leading to major problems in the application of modern thermohydrodynamic simulation software.
“The reliability of thermal conductivity data is often neglected in practice: Some believe that the error in these values has little effect on the calculation results, while others claim that they know everything about the thermal conductivity of rocks and can easily calculate it. Others resort to the default values in their simulators. We have previously shown that the lack of up-to-date thermophysical properties can lead to large errors of several tens of percent, for example, in predictions of heavy oil production—an unpleasant surprise for many experts. However, if the improvements we propose are applied to heat conduction modeling in simulators, the reliability of production predictions will increase significantly,” notes Skoltech lead researcher Yevgeny Chekhonin.
“Although our proposals to improve the quality of thermal conductivity modeling make this process more complex, it still clearly leads to significantly more reliable estimates of the target parameter. When it comes to porous and fractured reservoir rocks, such predictions are important not only for hydrocarbon extraction, but also for geothermal energy.Furthermore, reliable estimates of thermal conductivity are crucial for underground storage of radioactive waste, as this minimizes the risk of rock overheating, which could lead to further destruction and leakage of dangerous liquids to the earth’s surface,” Professor Popov adds.
Researchers use machine learning to aid oil production. More information: Z. Pichugin et al, A weighted geometric mean model for determining the thermal conductivity of reservoir rocks: Current issues with model applicability and modification, Geothermal (2022). DOI: 10.1016/j.geothermics.2022.102456 Provided by Skolkovo Institute of Science and Technology
Citation: Rock thermal conductivity model used by oil, gas and geothermal companies turns out to be unreliable (2022 July 28) Retrieved July 29, 2022 from
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