664 research outputs found
Modelling of deep wells thermal modes
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.ΠΠ΅ΡΠ°. ΠΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π²ΠΏΠ»ΠΈΠ²Ρ ΡΡΠ·Π½ΠΈΡ
ΡΠΌΠΎΠ² ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡΠ½Ρ ΡΠΈΡΠΊΡΠ»ΡΡΡΠΎΡ ΡΡΠ΄ΠΈΠ½ΠΈ Π½Π° ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΈΠΉ ΡΠ΅ΠΆΠΈΠΌ Π³Π»ΠΈΠ±ΠΎΠΊΠΈΡ
ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½.
ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ°. ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ΠΈ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ² ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡΠ½Ρ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΎΡ ΡΡ
Π΅ΠΌΠΈ Π΄ΠΎ ΡΠΎΠ·ΡΠ°Ρ
ΡΠ½ΠΊΡ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΡΠ²Π°Π²ΡΡ ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΈΠΉ ΡΠ΅ΠΆΠΈΠΌ Ρ Π²Π΅ΡΡΠΈΠΊΠ°Π»ΡΠ½ΡΠΉ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½Ρ Π· ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΠ½ΠΈΠΌ ΡΠΎΠ·ΡΠ°ΡΡΠ²Π°Π½Π½ΡΠΌ Π±ΡΡΠΈΠ»ΡΠ½ΠΎΡ ΠΊΠΎΠ»ΠΎΠ½ΠΈ. ΠΠ΅ΡΠ΅Π΄Π±Π°ΡΠ°Π»ΠΎΡΡ, ΡΠΎ ΡΡΡΠ½ΠΊΠΈ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ Π½Π°Π»Π΅ΠΆΠ½ΠΈΠΌ ΡΠΈΠ½ΠΎΠΌ ΡΠ·ΠΎΠ»ΡΠΎΠ²Π°Π½Ρ, ΠΏΡΠΈΠΏΠ»ΠΈΠ² Ρ Π²ΡΡΠ°ΡΠΈ ΡΡΠ΄ΠΈΠ½ΠΈ Π²ΡΠ΄ΡΡΡΠ½Ρ. ΠΠΎΠ΄Π΅Π»ΡΠ²Π°Π²ΡΡ ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ» ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡ Ρ ΠΏΠΎΡΠΎΠΊΠ°Ρ
Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΎΡ (Π²ΠΎΠ΄ΠΈ) ΡΠ° Π½Π΅Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΎΡ (Π³Π»ΠΈΠ½ΠΈΡΡΠΎΠ³ΠΎ ΡΠΎΠ·ΡΠΈΠ½Ρ) ΡΡΠ΄ΠΈΠ½ ΡΠ·Π΄ΠΎΠ²ΠΆ ΡΡΠΎΠ²Π±ΡΡΠ° ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ Π·ΠΌΡΠ½ΠΈ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΆΠΈΠΌΡ Π³ΡΡΡΡΠΊΠΈΡ
ΠΏΠΎΡΡΠ΄ Π· Π³Π»ΠΈΠ±ΠΈΠ½ΠΎΡ. ΠΠ»Ρ Π²Π΅ΡΠΈΡΡΠΊΠ°ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΡΠΎΠ·ΡΠ°Ρ
ΡΠ½ΠΊΡ Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΠΎΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π±ΡΠ² Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΈΠΉ ΠΏΠΎΡΡΠ²Π½ΡΠ»ΡΠ½ΠΈΠΉ Π°Π½Π°Π»ΡΠ· ΡΠΎΠ·ΡΠ°Ρ
ΡΠ½ΠΊΠΎΠ²ΠΈΡ
ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΈΡ
Π΄Π°Π½ΠΈΡ
Π· Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠΈ ΠΏΡΠΎΠΌΠΈΠ²Π½ΠΎΡ ΡΡΠ΄ΠΈΠ½ΠΈ Ρ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½Ρ.
Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° Ρ Π²Π΅ΡΠΈΡΡΡΡΠΉΠΎΠ²Π°Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΈΡ
ΠΏΠΎΠ»ΡΠ² Π· Π³Π»ΠΈΠ±ΠΈΠ½ΠΎΡ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ. ΠΡΡΠΈΠΌΠ°Π½ΠΎ ΡΡΠ°ΡΡΠΎΠ½Π°ΡΠ½ΠΈΠΉ ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ» ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡ ΡΠ·Π΄ΠΎΠ²ΠΆ ΡΡΠΎΠ²Π±ΡΡΠ° ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ Π΄Π»Ρ ΡΡΠ·Π½ΠΈΡ
ΡΠΈΠΏΡΠ² (Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΈΡ
Π°Π±ΠΎ Π½Π΅Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΈΡ
) ΡΠΈΡΠΊΡΠ»ΡΡΡΠΈΡ
ΡΡΠ΄ΠΈΠ½ ΠΏΡΠΈ Π»ΡΠ½ΡΠΉΠ½ΠΎΠΌΡ Π·Π°ΠΊΠΎΠ½Ρ Π·ΠΌΡΠ½ΠΈ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠΈ Π³ΡΡΡΡΠΊΠΈΡ
ΠΏΠΎΡΡΠ΄ Π· Π³Π»ΠΈΠ±ΠΈΠ½ΠΎΡ. ΠΠΈΡΠ²Π»Π΅Π½ΠΎ, ΡΠΎ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ° ΠΏΠΎΡΠΎΠΊΡ ΡΡΠ΄ΠΈΠ½ΠΈ Π½Π° Π²ΠΈΠ±ΠΎΡ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ Ρ Π½Π° Π²ΠΈΡ
ΠΎΠ΄Ρ Π½Π° Π΄Π΅Π½Π½Ρ ΠΏΠΎΠ²Π΅ΡΡ
Π½Ρ Π·Π°Π»Π΅ΠΆΠΈΡΡ Π²ΡΠ΄ ΡΠΈΠΏΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°Π½ΠΎΡ ΡΡΠ΄ΠΈΠ½ΠΈ Ρ ΡΠ΅ΠΆΠΈΠΌΡ ΡΠ΅ΡΡΡ. ΠΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΠΎ Π·Π° ΡΠ°Ρ
ΡΠ½ΠΎΠΊ ΡΠ΅ΡΠΌΠΎΡΠ·ΠΎΠ»ΡΡΡΡ ΠΊΠΎΠ»ΠΎΠ½ΠΈ Π±ΡΡΠΈΠ»ΡΠ½ΠΈΡ
ΡΡΡΠ± Π·Π½ΠΈΠΆΡΡΡΡΡΡ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡΠ½ ΠΌΡΠΆ Π½ΠΈΠ·Ρ
ΡΠ΄Π½ΠΈΠΌ Ρ Π²ΠΈΡΡ
ΡΠ΄Π½ΠΈΠΌ ΠΏΠΎΡΠΎΠΊΠ°ΠΌΠΈ, ΡΠΎ ΠΏΡΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡ Π΄ΠΎ Π·Π½ΠΈΠΆΠ΅Π½Π½Ρ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠΈ Π½ΠΈΠ·Ρ
ΡΠ΄Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠΎΠΊΡ Π½Π° Π²ΠΈΠ±ΠΎΡ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ΠΈ, Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΡΡΠΈ Π±ΡΠ»ΡΡ ΡΠΏΡΠΈΡΡΠ»ΠΈΠ²ΠΈΠΉ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΈΠΉ ΡΠ΅ΠΆΠΈΠΌ Π½Π° Π²ΠΈΠ±ΠΎΡ, ΡΠΊΠΈΠΉ ΡΠΏΡΠΈΡΡ ΠΊΡΠ°ΡΠΎΠΌΡ ΡΡΠΉΠ½ΡΠ²Π°Π½Π½Ρ ΠΏΠΎΡΠΎΠ΄ΠΈ ΡΠ° ΠΎΡ
ΠΎΠ»ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ ΠΏΡΠΈ Π±ΡΡΡΠ½Π½Ρ.
ΠΠ°ΡΠΊΠΎΠ²Π° Π½ΠΎΠ²ΠΈΠ·Π½Π°. ΠΠΈΡΠ²Π»Π΅Π½ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ»Ρ ΡΠ° Π·ΠΌΡΠ½ΠΈ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠΈ Π²Π·Π΄ΠΎΠ²ΠΆ ΡΡΠΎΠ²Π±ΡΡΠ° ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ ΠΏΡΠΈ ΡΡΠ°ΡΡΠΎΠ½Π°ΡΠ½ΠΎΠΌΡ ΡΠ΅ΠΆΠΈΠΌΡ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡΠ½Ρ Π² ΡΡΡΠ±ΡΠ»Π΅Π½ΡΠ½ΠΎΠΌΡ Ρ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠ°Ρ
ΡΠ΅ΡΡΡ ΡΠΊ Π΄Π»Ρ Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΈΡ
, ΡΠ°ΠΊ Ρ Π½Π΅Π½ΡΡΡΠΎΠ½ΡΠ²ΡΡΠΊΠΈΡ
ΡΠΈΡΠΊΡΠ»ΡΡΡΠΈΡ
ΡΡΠ΄ΠΈΠ½.
ΠΡΠ°ΠΊΡΠΈΡΠ½Π° Π·Π½Π°ΡΠΈΠΌΡΡΡΡ. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Ρ ΠΎΡΡΠΈΠΌΠ°Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ ΠΌΠΎΠΆΡΡΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ ΠΎΡΡΠ½ΠΎΡΠ½ΠΈΡ
ΡΠΎΠ·ΡΠ°Ρ
ΡΠ½ΠΊΡΠ² ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΈΡ
ΡΠ΅ΠΆΠΈΠΌΡΠ² ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½ ΡΠ° ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ Π· ΡΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ ΡΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΡΡΡΡ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΡΠ½Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΡΠ² Ρ ΡΠ²Π΅ΡΠ΄Π»ΠΎΠ²ΠΈΠ½Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ Π²ΠΈΠΌΠΎΠ³ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ.Π¦Π΅Π»Ρ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΠΉ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π° ΡΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠ΅ΠΉ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ Π½Π° ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ
ΡΠ΅ΠΆΠΈΠΌ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
ΡΠΊΠ²Π°ΠΆΠΈΠ½.
ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ°. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π°. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΡΡ
Π΅ΠΌΡ ΠΊ ΡΠ°ΡΡΠ΅ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΡΡ ΡΠ΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ ΡΠ΅ΠΆΠΈΠΌ Π² Π²Π΅ΡΡΠΈΠΊΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΊΠ²Π°ΠΆΠΈΠ½Π΅ Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΌ Π±ΡΡΠΈΠ»ΡΠ½ΠΎΠΉ ΠΊΠΎΠ»ΠΎΠ½Ρ. ΠΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π»ΠΎΡΡ, ΡΡΠΎ ΡΡΠ΅Π½ΠΊΠΈ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Π½Π°Π΄Π»Π΅ΠΆΠ°ΡΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ ΠΈΠ·ΠΎΠ»ΠΈΡΠΎΠ²Π°Π½Ρ, ΠΏΡΠΈΡΠΎΠΊ ΠΈ ΠΏΠΎΡΠ΅ΡΠΈ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ ΠΎΡΡΡΡΡΡΠ²ΡΡΡ. ΠΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π»ΠΎΡΡ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡ Π² ΠΏΠΎΡΠΎΠΊΠ°Ρ
Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΎΠΉ (Π²ΠΎΠ΄Ρ) ΠΈ Π½Π΅Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΎΠΉ (Π³Π»ΠΈΠ½ΠΈΡΡΠΎΠ³ΠΎ ΡΠ°ΡΡΠ²ΠΎΡΠ°) ΠΆΠΈΠ΄ΠΊΠΎΡΡΠ΅ΠΉ Π²Π΄ΠΎΠ»Ρ ΡΡΠ²ΠΎΠ»Π° ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ° Π³ΠΎΡΠ½ΡΡ
ΠΏΠΎΡΠΎΠ΄ Ρ Π³Π»ΡΠ±ΠΈΠ½ΠΎΠΉ. ΠΠ»Ρ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΡΠ°ΡΡΠ΅ΡΠ° ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π±ΡΠ» Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ°ΡΡΠ΅ΡΠ½ΡΡ
ΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ ΠΏΡΠΎΠΌΡΠ²ΠΎΡΠ½ΠΎΠΉ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ Π² ΡΠΊΠ²Π°ΠΆΠΈΠ½Π΅.
Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΈ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΡΡ
ΠΏΠΎΠ»Π΅ΠΉ ΠΏΠΎ Π³Π»ΡΠ±ΠΈΠ½Π΅ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ. ΠΠΎΠ»ΡΡΠ΅Π½ΠΎ ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ½ΠΎΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡ Π²Π΄ΠΎΠ»Ρ ΡΡΠ²ΠΎΠ»Π° ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ Π΄Π»Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΠΏΠΎΠ² (Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ
ΠΈΠ»ΠΈ Π½Π΅Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ
) ΡΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠΈΡ
ΠΆΠΈΠ΄ΠΊΠΎΡΡΠ΅ΠΉ ΠΏΡΠΈ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΌ Π·Π°ΠΊΠΎΠ½Π΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ Π³ΠΎΡΠ½ΡΡ
ΠΏΠΎΡΠΎΠ΄ Ρ Π³Π»ΡΠ±ΠΈΠ½ΠΎΠΉ. ΠΡΡΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ° ΠΏΠΎΡΠΎΠΊΠ° ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ Π½Π° Π·Π°Π±ΠΎΠ΅ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ ΠΈ Π½Π° Π²ΡΡ
ΠΎΠ΄Π΅ Π½Π° Π΄Π½Π΅Π²Π½ΡΡ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΠΈΠΏΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠΎΠΉ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ ΠΈ ΡΠ΅ΠΆΠΈΠΌΠ° ΡΠ΅ΡΠ΅Π½ΠΈΡ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ Π·Π° ΡΡΠ΅Ρ ΡΠ΅ΡΠΌΠΎΠΈΠ·ΠΎΠ»ΡΡΠΈΠΈ ΠΊΠΎΠ»ΠΎΠ½Ρ Π±ΡΡΠΈΠ»ΡΠ½ΡΡ
ΡΡΡΠ± ΡΠ½ΠΈΠΆΠ°Π΅ΡΡΡ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½ ΠΌΠ΅ΠΆΠ΄Ρ Π½ΠΈΡΡ
ΠΎΠ΄ΡΡΠΈΠΌ ΠΈ Π²ΠΎΡΡ
ΠΎΠ΄ΡΡΠΈΠΌ ΠΏΠΎΡΠΎΠΊΠ°ΠΌΠΈ, ΡΡΠΎ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ Π½ΠΈΡΡ
ΠΎΠ΄ΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΠΎΠΊΠ° Π½Π° Π·Π°Π±ΠΎΠ΅ ΡΠΊΠ²Π°ΠΆΠΈΠ½Ρ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Ρ Π±ΠΎΠ»Π΅Π΅ Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΡΠΉ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ½ΡΠΉ ΡΠ΅ΠΆΠΈΠΌ Π½Π° Π·Π°Π±ΠΎΠ΅, ΠΊΠΎΡΠΎΡΡΠΉ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ Π»ΡΡΡΠ΅ΠΌΡ ΡΠ°Π·ΡΡΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠΎΠ΄Ρ ΠΈ ΠΎΡ
Π»Π°ΠΆΠ΄Π΅Π½ΠΈΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ° ΠΏΡΠΈ Π±ΡΡΠ΅Π½ΠΈΠΈ.
ΠΠ°ΡΡΠ½Π°Ρ Π½ΠΎΠ²ΠΈΠ·Π½Π°. ΠΡΡΠ²Π»Π΅Π½ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ Π²Π΄ΠΎΠ»Ρ ΡΡΠ²ΠΎΠ»Π° ΡΠΊΠ²Π°ΠΆΠΈΠ½ ΠΏΡΠΈ ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ½ΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π° Π² ΡΡΡΠ±ΡΠ»Π΅Π½ΡΠ½ΠΎΠΌ ΠΈ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΌ ΡΠ΅ΠΆΠΈΠΌΠ°Ρ
ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΊΠ°ΠΊ Π΄Π»Ρ Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ
, ΡΠ°ΠΊ ΠΈ Π½Π΅Π½ΡΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ
ΡΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠΈΡ
ΠΆΠΈΠ΄ΠΊΠΎΡΡΠ΅ΠΉ.
ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Π°Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΎΡΠ½ΡΡ
ΡΠ°ΡΡΠ΅ΡΠΎΠ² ΡΠ΅ΠΏΠ»ΠΎΠ²ΡΡ
ΡΠ΅ΠΆΠΈΠΌΠΎΠ² ΡΠΊΠ²Π°ΠΆΠΈΠ½ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ ΠΏΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡΡ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π² ΡΠΊΠ²Π°ΠΆΠΈΠ½Π΅ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΉ ΡΠ΅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
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)
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 βtraditionalβ factors affecting face alignment performance like large pose, initialization and resolution, and introduce a βnewβ 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.
第3εζ₯΅εη§ε¦γ·γ³γγΈγ¦γ /第35εεζ₯΅ιη³γ·γ³γγΈγ¦γ 11ζ29ζ₯οΌζ¨οΌγ30ζ₯οΌιοΌ ε½η«ε½θͺη η©Άζ 2ιθ¬
Optimized Effective Potentials in Finite Basis Sets
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
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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