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A set of advanced methods and instruments was developed to improve essentially quality of experimental data on reservoir thermal properties (thermal conductivity, thermal diffusivity, volumetric heat capacity, coefficient of linear thermal expansion) at atmospheric and reservoir thermobaric conditions. The new thermal core logging technique provides continuous high-resolution profiling rock thermal properties along wells accounting for rock anisotropy and multi-scale heterogeneity. Integration of the technique application results with standard well logging data leads to possibilities of high resolution profiling porosity, rock matrix thermal properties, elastic wave velocities and modulus, rock density, etc. New approaches are described that allow indirect determination of the reservoir thermal properties from standard petrophysical logging data accounting for formation anisotropy. A new laser optical scanning instrument, enhanced theoretical modeling of effective thermal properties and special workflow opened a way to determination of combination for rock thermal properties on rock cuttings at formation temperatures. The new experimental basis improves reliability of data on physical properties of reservoirs, results of specific heat flow determination and reservoir thermal regime modeling within prospecting, exploration and development of geothermal energy fields.

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Reliable geothermal data are required for basin and petroleum system modeling. The essential shortcomings of the methods and results of previous geothermal investigations lead to a necessity to reappraise the data on the thermal properties and heat flow. A new, advanced experimental basis was used to provide reliable data on vertical variations in the thermal properties of formation and heat flow for the area surrounding a prospecting borehole drilled through an unconventional hydrocarbon reservoir of the Domanik Formation in the Orenburg region (Russia). Temperature logging was conducted 12.5 months after well drilling. The thermal properties of the rocks were measured with continuous thermal core profiling on all 1699 recovered core samples. Within non-cored intervals, the thermal conductivity of the rocks was determined from well-logging data. The influence of core aging, multiscale heterogeneity and anisotropy, in situ pressure and temperature on the thermal properties of rock was accounted for. The terrestrial heat flow was determined to be 72.6 ± 2.2 mW·m−2—~114% larger than the published average data for the studied area. The experiment presents the first experience of supporting basin modeling in unconventional plays with advanced experimental geothermal investigations.

SUMMARY Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there are only several studies performed to assess the applicability of neural network-based algorithms to predict rock thermal conductivity from well-logging data. In this research, we aim to define the most effective machine-learning algorithms for well-log based determination of rock thermal conductivity. Well-logging data acquired at a heavy oil reservoir together with results of thermal logging on cores extracted from two wells were the basis for our research. Eight different regression models were developed and tested to predict vertical variations of rock conductivity from well-logging data. Additionally, rock thermal conductivity was determined based on Lichtenecker–Asaad model. Comparison study of regression-based and theoretical-based approaches was performed. Among considered machine learning techniques Random Forest algorithm was found to be the most accurate at well-log based determination of rock thermal conductivity. From a comparison of the thermal conductivity—depth profile predicted from well-logging data with the experimental data, and it can be concluded that thermal conductivity can be determined with a total relative error of 12.54 per cent. The obtained results prove that rock thermal conductivity can be inferred from well-logging data for wells that are drilled in a similar geological setting based on the Random Forest algorithm with an accuracy sufficient for industrial needs.