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    Modelling of deep wells thermal modes

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    Purpose. Investigation of various heat-exchange conditions influence of the tower liquid on the deep wells thermal conditions. Methods. Methods of heat-exchange processes mathematical modeling are used. On the basis of the developed scheme for calculation, the thermal condition in a vertical well with a concentric arrangement of the drill-string was investigated. It was assumed that the walls of the well are properly insulated, and there is no flow or loss of fluid. The temperature distribution in the Newtonian (water) and non-Newtonian (clay mud) liquid along the borehole was simulated taking into account changes in the temperature regime of rocks with depth. To verify the calculation method and determine the reliability of the results, a comparative analysis of the calculated and experimental data to determine the temperature of the drilling liquid in the well was performed. Findings. A mathematical model for the study of temperature fields along the well depth was proposed and verified. A steady-state temperature distribution along the borehole is obtained for various types (Newtonian or non-Newtonian) tower liquid, with a linear law of change in rocks temperature with depth. It has been established that the temperature of the liquid flow at the face of hole and at the exit to the surface depends on the type of liquid used and the flow regime. It has been established that due to thermal insulation of drill pipe columns, heat-exchange between the downward and upward flow is reduced, which leads to a decrease in the temperature of the downward flow at the face of hole, providing a more favorable temperature at the face, which contributes to better destruction of the rock and cooling the tool during drilling. Originality. The nature of temperature distribution and changes along the borehole under the steady-state mode of heat-exchange in a turbulent and structural flow regime for both Newtonian and non-Newtonian circulating liquid are revealed. Practical implications. The proposed mathematical model and obtained results can be used to conduct estimates of the thermal conditions of wells and the development of recommendations for controlling the intensity of heat-exchange processes in the well, in accordance with the requirements of a specific technology.ΠœΠ΅Ρ‚Π°. ДослідТСння Π²ΠΏΠ»ΠΈΠ²Ρƒ Ρ€Ρ–Π·Π½ΠΈΡ… ΡƒΠΌΠΎΠ² Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡ–Π½Ρƒ Ρ†ΠΈΡ€ΠΊΡƒΠ»ΡŽΡŽΡ‡ΠΎΡ— Ρ€Ρ–Π΄ΠΈΠ½ΠΈ Π½Π° Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ Π³Π»ΠΈΠ±ΠΎΠΊΠΈΡ… свСрдловин. ΠœΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ°. Використано ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈ ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΎΠ³ΠΎ модСлювання процСсів Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡ–Π½Ρƒ. На основі Ρ€ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½ΠΎΡ— схСми Π΄ΠΎ Ρ€ΠΎΠ·Ρ€Π°Ρ…ΡƒΠ½ΠΊΡƒ дослідТувався Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ Ρƒ Π²Π΅Ρ€Ρ‚ΠΈΠΊΠ°Π»ΡŒΠ½Ρ–ΠΉ свСрдловині Π· ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€ΠΈΡ‡Π½ΠΈΠΌ Ρ€ΠΎΠ·Ρ‚Π°ΡˆΡƒΠ²Π°Π½Π½ΡΠΌ Π±ΡƒΡ€ΠΈΠ»ΡŒΠ½ΠΎΡ— ΠΊΠΎΠ»ΠΎΠ½ΠΈ. ΠŸΠ΅Ρ€Π΅Π΄Π±Π°Ρ‡Π°Π»ΠΎΡΡ, Ρ‰ΠΎ стінки свСрдловини Π½Π°Π»Π΅ΠΆΠ½ΠΈΠΌ Ρ‡ΠΈΠ½ΠΎΠΌ Ρ–Π·ΠΎΠ»ΡŒΠΎΠ²Π°Π½Ρ–, ΠΏΡ€ΠΈΠΏΠ»ΠΈΠ² Ρ– Π²Ρ‚Ρ€Π°Ρ‚ΠΈ Ρ€Ρ–Π΄ΠΈΠ½ΠΈ відсутні. МодСлювався Ρ€ΠΎΠ·ΠΏΠΎΠ΄Ρ–Π» Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ Ρƒ ΠΏΠΎΡ‚ΠΎΠΊΠ°Ρ… Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΎΡ— (Π²ΠΎΠ΄ΠΈ) Ρ‚Π° Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΎΡ— (глинистого Ρ€ΠΎΠ·Ρ‡ΠΈΠ½Ρƒ) Ρ€Ρ–Π΄ΠΈΠ½ ΡƒΠ·Π΄ΠΎΠ²ΠΆ стовбура свСрдловини Π· урахуванням Π·ΠΌΡ–Π½ΠΈ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΎΠ³ΠΎ Ρ€Π΅ΠΆΠΈΠΌΡƒ Π³Ρ–Ρ€ΡΡŒΠΊΠΈΡ… ΠΏΠΎΡ€Ρ–Π΄ Π· глибиною. Для Π²Π΅Ρ€ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ Ρ€ΠΎΠ·Ρ€Π°Ρ…ΡƒΠ½ΠΊΡƒ Ρ– визначСння достовірності Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π² Π±ΡƒΠ² Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΈΠΉ ΠΏΠΎΡ€Ρ–Π²Π½ΡΠ»ΡŒΠ½ΠΈΠΉ Π°Π½Π°Π»Ρ–Π· Ρ€ΠΎΠ·Ρ€Π°Ρ…ΡƒΠ½ΠΊΠΎΠ²ΠΈΡ… Ρ‚Π° Π΅ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½ΠΈΡ… Π΄Π°Π½ΠΈΡ… Π· визначСння Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ΠΈ ΠΏΡ€ΠΎΠΌΠΈΠ²Π½ΠΎΡ— Ρ€Ρ–Π΄ΠΈΠ½ΠΈ Ρƒ свСрдловині. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ. Π—Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° Ρ– Π²Π΅Ρ€ΠΈΡ„Ρ–Ρ†Ρ–ΠΉΠΎΠ²Π°Π½Π° ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Π° модСль для дослідТСння Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΈΡ… ΠΏΠΎΠ»Ρ–Π² Π· глибиною свСрдловини. ΠžΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΎ стаціонарний Ρ€ΠΎΠ·ΠΏΠΎΠ΄Ρ–Π» Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ ΡƒΠ·Π΄ΠΎΠ²ΠΆ стовбура свСрдловини для Ρ€Ρ–Π·Π½ΠΈΡ… Ρ‚ΠΈΠΏΡ–Π² (Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΈΡ… Π°Π±ΠΎ Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΈΡ…) Ρ†ΠΈΡ€ΠΊΡƒΠ»ΡŽΡŽΡ‡ΠΈΡ… Ρ€Ρ–Π΄ΠΈΠ½ ΠΏΡ€ΠΈ Π»Ρ–Π½Ρ–ΠΉΠ½ΠΎΠΌΡƒ Π·Π°ΠΊΠΎΠ½Ρ– Π·ΠΌΡ–Π½ΠΈ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ΠΈ Π³Ρ–Ρ€ΡΡŒΠΊΠΈΡ… ΠΏΠΎΡ€Ρ–Π΄ Π· глибиною. ВиявлСно, Ρ‰ΠΎ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π° ΠΏΠΎΡ‚ΠΎΠΊΡƒ Ρ€Ρ–Π΄ΠΈΠ½ΠΈ Π½Π° Π²ΠΈΠ±ΠΎΡ— свСрдловини Ρ– Π½Π° Π²ΠΈΡ…ΠΎΠ΄Ρ– Π½Π° Π΄Π΅Π½Π½Ρƒ ΠΏΠΎΠ²Π΅Ρ€Ρ…Π½ΡŽ Π·Π°Π»Π΅ΠΆΠΈΡ‚ΡŒ Π²Ρ–Π΄ Ρ‚ΠΈΠΏΡƒ використовуваної Ρ€Ρ–Π΄ΠΈΠ½ΠΈ Ρ– Ρ€Π΅ΠΆΠΈΠΌΡƒ Ρ‚Π΅Ρ‡Ρ–Ρ—. ВстановлСно, Ρ‰ΠΎ Π·Π° Ρ€Π°Ρ…ΡƒΠ½ΠΎΠΊ тСрмоізоляції ΠΊΠΎΠ»ΠΎΠ½ΠΈ Π±ΡƒΡ€ΠΈΠ»ΡŒΠ½ΠΈΡ… Ρ‚Ρ€ΡƒΠ± Π·Π½ΠΈΠΆΡƒΡ”Ρ‚ΡŒΡΡ Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡ–Π½ ΠΌΡ–ΠΆ Π½ΠΈΠ·Ρ…Ρ–Π΄Π½ΠΈΠΌ Ρ– висхідним ΠΏΠΎΡ‚ΠΎΠΊΠ°ΠΌΠΈ, Ρ‰ΠΎ ΠΏΡ€ΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚ΡŒ Π΄ΠΎ зниТСння Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ΠΈ Π½ΠΈΠ·Ρ…Ρ–Π΄Π½ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΡƒ Π½Π° Π²ΠΈΠ±ΠΎΡ— свСрдловини, Π·Π°Π±Π΅Π·ΠΏΠ΅Ρ‡ΡƒΡŽΡ‡ΠΈ Π±Ρ–Π»ΡŒΡˆ сприятливий Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ Π½Π° Π²ΠΈΠ±ΠΎΡ—, який сприяє ΠΊΡ€Π°Ρ‰ΠΎΠΌΡƒ руйнування ΠΏΠΎΡ€ΠΎΠ΄ΠΈ Ρ‚Π° ΠΎΡ…ΠΎΠ»ΠΎΠ΄ΠΆΠ΅Π½Π½ΡŽ інструмСнту ΠΏΡ€ΠΈ Π±ΡƒΡ€Ρ–Π½Π½Ρ–. Наукова Π½ΠΎΠ²ΠΈΠ·Π½Π°. ВиявлСно Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ Ρ€ΠΎΠ·ΠΏΠΎΠ΄Ρ–Π»Ρƒ Ρ‚Π° Π·ΠΌΡ–Π½ΠΈ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ΠΈ Π²Π·Π΄ΠΎΠ²ΠΆ стовбура свСрдловин ΠΏΡ€ΠΈ стаціонарному Ρ€Π΅ΠΆΠΈΠΌΡ– Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡ–Π½Ρƒ Π² Ρ‚ΡƒΡ€Π±ΡƒΠ»Π΅Π½Ρ‚Π½ΠΎΠΌΡƒ Ρ– структурному Ρ€Π΅ΠΆΠΈΠΌΠ°Ρ… Ρ‚Π΅Ρ‡Ρ–Ρ— як для Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΈΡ…, Ρ‚Π°ΠΊ Ρ– Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½Ρ–Π²ΡΡŒΠΊΠΈΡ… Ρ†ΠΈΡ€ΠΊΡƒΠ»ΡŽΡŽΡ‡ΠΈΡ… Ρ€Ρ–Π΄ΠΈΠ½. ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π½Π° Π·Π½Π°Ρ‡ΠΈΠΌΡ–ΡΡ‚ΡŒ. Π—Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Π° модСль Ρ– ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½Ρ– Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ ΠΌΠΎΠΆΡƒΡ‚ΡŒ використовуватися для провСдСння ΠΎΡ†Ρ–Π½ΠΎΡ‡Π½ΠΈΡ… Ρ€ΠΎΠ·Ρ€Π°Ρ…ΡƒΠ½ΠΊΡ–Π² Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΈΡ… Ρ€Π΅ΠΆΠΈΠΌΡ–Π² свСрдловин Ρ‚Π° Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ Ρ€Π΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°Ρ†Ρ–ΠΉ Π· управління Ρ–Π½Ρ‚Π΅Π½ΡΠΈΠ²Π½Ρ–ΡΡ‚ΡŽ Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡ–Π½Π½ΠΈΡ… процСсів Ρƒ свСрдловині Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π½ΠΎ Π΄ΠΎ Π²ΠΈΠΌΠΎΠ³ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΡ— Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ—.ЦСль. ИсслСдованиС влияния Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… условий Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π° Ρ†ΠΈΡ€ΠΊΡƒΠ»ΠΈΡ€ΡƒΡŽΡ‰Π΅ΠΉ Тидкости Π½Π° Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ Ρ€Π΅ΠΆΠΈΠΌ Π³Π»ΡƒΠ±ΠΎΠΊΠΈΡ… скваТин. ΠœΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ°. Π˜ΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ матСматичСского модСлирования процСссов Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π°. На основС Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΉ схСмы ΠΊ расчСту исслСдовался Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ Ρ€Π΅ΠΆΠΈΠΌ Π² Π²Π΅Ρ€Ρ‚ΠΈΠΊΠ°Π»ΡŒΠ½ΠΎΠΉ скваТинС с концСнтричСским располоТСниСм Π±ΡƒΡ€ΠΈΠ»ΡŒΠ½ΠΎΠΉ ΠΊΠΎΠ»ΠΎΠ½Ρ‹. ΠŸΡ€Π΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π»ΠΎΡΡŒ, Ρ‡Ρ‚ΠΎ стСнки скваТины Π½Π°Π΄Π»Π΅ΠΆΠ°Ρ‰ΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ ΠΈΠ·ΠΎΠ»ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹, ΠΏΡ€ΠΈΡ‚ΠΎΠΊ ΠΈ ΠΏΠΎΡ‚Π΅Ρ€ΠΈ Тидкости ΠΎΡ‚ΡΡƒΡ‚ΡΡ‚Π²ΡƒΡŽΡ‚. ΠœΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π»ΠΎΡΡŒ распрСдСлСниС Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ Π² ΠΏΠΎΡ‚ΠΎΠΊΠ°Ρ… Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΎΠΉ (Π²ΠΎΠ΄Ρ‹) ΠΈ Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΎΠΉ (глинистого раствора) ТидкостСй вдоль ствола скваТины с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ измСнСния Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΎΠ³ΠΎ Ρ€Π΅ΠΆΠΈΠΌΠ° Π³ΠΎΡ€Π½Ρ‹Ρ… ΠΏΠΎΡ€ΠΎΠ΄ с Π³Π»ΡƒΠ±ΠΈΠ½ΠΎΠΉ. Для Π²Π΅Ρ€ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ расчСта ΠΈ опрСдСлСния достовСрности Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² Π±Ρ‹Π» Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· расчСтных ΠΈ ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΡŽ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ ΠΏΡ€ΠΎΠΌΡ‹Π²ΠΎΡ‡Π½ΠΎΠΉ Тидкости Π² скваТинС. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΈ Π²Π΅Ρ€ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π° матСматичСская модСль для исслСдования Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½Ρ‹Ρ… ΠΏΠΎΠ»Π΅ΠΉ ΠΏΠΎ Π³Π»ΡƒΠ±ΠΈΠ½Π΅ скваТины. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½ΠΎ стационарноС распрСдСлСниС Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€ вдоль ствола скваТины для Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² (Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ… ΠΈΠ»ΠΈ Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ…) Ρ†ΠΈΡ€ΠΊΡƒΠ»ΠΈΡ€ΡƒΡŽΡ‰ΠΈΡ… ТидкостСй ΠΏΡ€ΠΈ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΌ Π·Π°ΠΊΠΎΠ½Π΅ измСнСния Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ Π³ΠΎΡ€Π½Ρ‹Ρ… ΠΏΠΎΡ€ΠΎΠ΄ с Π³Π»ΡƒΠ±ΠΈΠ½ΠΎΠΉ. ВыявлСно, Ρ‡Ρ‚ΠΎ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π° ΠΏΠΎΡ‚ΠΎΠΊΠ° Тидкости Π½Π° Π·Π°Π±ΠΎΠ΅ скваТины ΠΈ Π½Π° Π²Ρ‹Ρ…ΠΎΠ΄Π΅ Π½Π° Π΄Π½Π΅Π²Π½ΡƒΡŽ ΠΏΠΎΠ²Π΅Ρ€Ρ…Π½ΠΎΡΡ‚ΡŒ зависит ΠΎΡ‚ Ρ‚ΠΈΠΏΠ° ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΠΎΠΉ Тидкости ΠΈ Ρ€Π΅ΠΆΠΈΠΌΠ° тСчСния. УстановлСно, Ρ‡Ρ‚ΠΎ Π·Π° счСт тСрмоизоляции ΠΊΠΎΠ»ΠΎΠ½Ρ‹ Π±ΡƒΡ€ΠΈΠ»ΡŒΠ½Ρ‹Ρ… Ρ‚Ρ€ΡƒΠ± сниТаСтся Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½ ΠΌΠ΅ΠΆΠ΄Ρƒ нисходящим ΠΈ восходящим ΠΏΠΎΡ‚ΠΎΠΊΠ°ΠΌΠΈ, Ρ‡Ρ‚ΠΎ ΠΏΡ€ΠΈΠ²ΠΎΠ΄ΠΈΡ‚ ΠΊ сниТСнию Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ нисходящСго ΠΏΠΎΡ‚ΠΎΠΊΠ° Π½Π° Π·Π°Π±ΠΎΠ΅ скваТины, обСспСчивая Π±ΠΎΠ»Π΅Π΅ благоприятный Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½Ρ‹ΠΉ Ρ€Π΅ΠΆΠΈΠΌ Π½Π° Π·Π°Π±ΠΎΠ΅, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ способствуСт Π»ΡƒΡ‡ΡˆΠ΅ΠΌΡƒ Ρ€Π°Π·Ρ€ΡƒΡˆΠ΅Π½ΠΈΡŽ ΠΏΠΎΡ€ΠΎΠ΄Ρ‹ ΠΈ ΠΎΡ…Π»Π°ΠΆΠ΄Π΅Π½ΠΈΡŽ инструмСнта ΠΏΡ€ΠΈ Π±ΡƒΡ€Π΅Π½ΠΈΠΈ. Научная Π½ΠΎΠ²ΠΈΠ·Π½Π°. ВыявлСн Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ распрСдСлСния ΠΈ измСнСния Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Ρ‹ вдоль ствола скваТин ΠΏΡ€ΠΈ стационарном Ρ€Π΅ΠΆΠΈΠΌΠ΅ Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π° Π² Ρ‚ΡƒΡ€Π±ΡƒΠ»Π΅Π½Ρ‚Π½ΠΎΠΌ ΠΈ структурном Ρ€Π΅ΠΆΠΈΠΌΠ°Ρ… тСчСния ΠΊΠ°ΠΊ для Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ…, Ρ‚Π°ΠΊ ΠΈ Π½Π΅Π½ΡŒΡŽΡ‚ΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ… Ρ†ΠΈΡ€ΠΊΡƒΠ»ΠΈΡ€ΡƒΡŽΡ‰ΠΈΡ… ТидкостСй. ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π΅ΡΠΊΠ°Ρ Π·Π½Π°Ρ‡ΠΈΠΌΠΎΡΡ‚ΡŒ. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Π°Ρ матСматичСская модСль ΠΈ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒΡΡ для провСдСния ΠΎΡ†Π΅Π½ΠΎΡ‡Π½Ρ‹Ρ… расчСтов Ρ‚Π΅ΠΏΠ»ΠΎΠ²Ρ‹Ρ… Ρ€Π΅ΠΆΠΈΠΌΠΎΠ² скваТин ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Ρ€Π΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°Ρ†ΠΈΠΉ ΠΏΠΎ ΡƒΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΡŽ ΠΈΠ½Ρ‚Π΅Π½ΡΠΈΠ²Π½ΠΎΡΡ‚ΡŒΡŽ Ρ‚Π΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π½Ρ‹Ρ… процСссов Π² скваТинС Π² соотвСтствии с трСбованиями ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΉ Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ.The authors thank the Institute of Geotechnical Mechanics named by N. Poljakov of National Academy of Sciences of Ukraine (Dnipro, Ukraine) for providing technical and informational support in this work

    Language-Aware Soft Prompting: Text-to-Text Optimization for Fewand Zero-Shot Adaptation of V&L Models

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    Soft prompt learning has emerged as a promising direction for adapting V &L models to a downstream task using a few training examples. However, current methods significantly overfit the training data suffering from large accuracy degradation when tested on unseen classes from the same domain. In addition, all prior methods operate exclusively under the assumption that both vision and language data is present. To this end, we make the following 5 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we also propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) Moreover, we identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) Importantly, we show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Expanding for the first time the setting to language-only adaptation, (5) we present a novel zero-shot variant of LASP where no visual samples at all are available for the downstream task. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Finally, (c) we show that our zero-shot variant improves upon CLIP without requiring any extra data. Code will be made available

    How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks)

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    This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b)We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all β€œtraditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a β€œnew” one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from https: //www.adrianbulat.com/face-alignment

    Snow cover of Central East Antarctica (Vostok station) as an ideal natural spot for collecting Cosmic Dust: preliminary results on recovery of chondritic micrometeorites.

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    第3ε›žζ₯΅εŸŸη§‘学シンポジウム/第35ε›žε—ζ₯΅ιš•ηŸ³γ‚·γƒ³γƒγ‚Έγ‚¦γƒ  11月29ζ—₯οΌˆζœ¨οΌ‰γ€30ζ—₯οΌˆι‡‘οΌ‰ ε›½η«‹ε›½θͺžη ”穢所 2ιšŽθ¬›

    Optimized Effective Potentials in Finite Basis Sets

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    The finite basis optimized effective potential (OEP) method within density functional theory is examined as an ill-posed problem. It is shown that the generation of nonphysical potentials is a controllable manifestation of the use of unbalanced, and thus unsuitable, basis sets. A modified functional incorporating a regularizing smoothness measure of the OEP is introduced. This provides a condition on balanced basis sets for the potential, as well as a method to determine the most appropriate OEP potential and energy from calculations performed with any finite basis set.Comment: 23 pages, 28 figure

    Integral Human Pose Regression

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    State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time
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