176 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
Recommended from our members
Control of mosaic disease using microbial biostimulants: insights from mathematical modelling
A major challenge to successful crop production comes from viral diseases of plants that cause significant crop losses, threatening global food security and the livelihoods of countries that rely on those crops for their staple foods or source of income. One example of such diseases is a mosaic disease of plants, which is caused by begomoviruses and is spread to plants by whitefly. In order to mitigate negative impact of mosaic disease, several different strategies have been employed over the years, including roguing/replanting of plants, as well as using pesticides, which have recently been shown to be potentially dangerous to the environment and humans. In this paper we derive and analyse a mathematical model for control of mosaic disease using natural microbial biostimulants that, besides improving plant growth, protect plants against infection through a mechanism of RNA interference. By analysing the stability of the systemβs steady states, we will show how properties of biostimulants affect disease dynamics, and in particular, how they determine whether the mosaic disease is eradicated or is rather maintained at some steady level. We will also present the results of numerical simulations that illustrate the behaviour of the model in different dynamical regimes, and discuss biological implications of theoretical results for the practical purpose of control of mosaic disease
Psychological and Pedagogical Aspects of the Development of Integrative Readiness of Future Specialists for Professional Activity
The need to develop an integrative readiness of future specialists is a relevant scientific problem. The reasons for this could be based on the fact that that the specialists-to-be were expected to be involved in the fierce competition for vacancies and areas of activity, have modern information and communication tools, i.e. have an integrative readiness for professional activity. Institutions do not, however, have a single integration (interdisciplinary) framework for training, do not provide a comprehensive educational information, technical tools, strategies and technologies of education, reasoned psychological and pedagogical conditions. According to the author, the integrative readiness of future specialists for professional activity is a system-personality formation that reflects the unity of theoretical and managerial training and practical ability of students to comprehensively apply regulatory, socio-economic, psychological and pedagogical methods and technologies for solving different problems. This readiness reflects the unity of the motivational inclination of future specialists to professional activity and knowledge of practical technologies for solving a wide range of professional problems in personal and business interactions. The research methodology is based on the concept of key competence, which provides systematization, classification of significant problems, development of a matrix of significant problems, and determination of overall strategy, management technology of professional training development process. Students and teachers can use research materials can be used by in educational and practical activities; developers of content, organizational forms and methods of professional training to improve the practical component of curricula and standards of their development
Comparison of ratioing and RCNA methods in the detection of flooded areas using Sentinel 2 Imagery (case study: Tulun, Russia)
Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographicβecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes.LA/P/0069/2020info:eu-repo/semantics/publishedVersio
Rare steppe plant communities in Ukraine: Status, threats and their minimization
Nowadays, the impact of anthropogenic activities on natural vegetation is constantly increasing, the level of threats is raised, and newer risk factors are emerging. Recent trends in the anthropogenic impact on plant communities are extremely pronounced, especially on those listed in the Green Book of Ukraine (GBU). Identifying such trends is required for the further development of strategic and tactical planning for the preservation and restoration of rare grass, shrub, and subshrub steppe, petrophyte and psammophyte plant communities of the steppe and forest-steppe zones of Ukraine. In addition to well-established threat factors that cause changes in the habitat of plant communities or mechanically affect plants, new specific threats occur. Today, the most important among them in Ukraine are as follows: climate change, military activity, growing population in the industrial cities, population poverty and government corruption, changes in forms of land ownership and the creation of a land market, lack of knowledge and effective policy, including lack of popular scientific information about the status of specific species and plant groupings, inadequate management of protected areas, uncoordinated environmental protection measures, ineffective sanctions, insufficient monitoring of the consumption of natural biological resources. The preservation of rare grass, shrubby and subshrubby communities in the steppe zone of Ukraine should be provided with proper support at the state level. There is a pressing need for a law of Ukraine βOn the preservation of the steppes in Ukraineβ and this will require mechanisms for its implementation. The issue of developing a strategy for the conservation and balanced use of steppe ecosystems in Ukraine, whose area is one of the largest in Europe, has long been raised. The main goal of the strategy is the actual preservation of steppe communities (most of which are currently rare) ensuring their restoration, minimizing degradation, and stopping biodiversity loss. In order to develop specific actions to eliminate threats or reduce their impact on rare plant groupings, it is required to investigate the causes of threats and assess their level and duration. This is required to preserve the landscape and biotic diversity in the steppe zone of Ukraine
Substantiation of thermomechanical technology parameters of absorbing levels isolation of the boreholes
The aim of the work is to improve the thermomechanical absorption insulation technology horizons of drilling wells by the established regularities of change and the substantiation of its regime parameters from the composition and physical-mechanical properties strengthen thermoplastic composite material and, on this basis, development a technological regulation containing recommendations on the manufacture of composites and organizations laying work, designing and isolation of the absorption zones of the washing liquid in the drilling rigs wells. The tasks set were solved by complex method research that contains analysis and synthesis of literary and patent sources, conducting analytical, experimental and industrial research. Experimental processing data was carried out using methods of mathematical statistics. Experimental research is carried out using the provisions of the theory of scientific experiment and theory random processes. The evaluation of the effectiveness of the results was carried out in production conditions
Disturbance indicator values for European plants
Motivation Indicator values are numerical values used to characterize the ecological niches of species and to estimate their occurrence along gradients. Indicator values on climatic and edaphic niches of plant species have received considerable attention in ecological research, whereas data on the optimal positioning of species along disturbance gradients are less developed. Here, we present a new data set of disturbance indicator values identifying optima along gradients of natural and anthropogenic disturbance for 6382 vascular plant species based on the analysis of 736,366 European vegetation plots and using expert-based characterization of disturbance regimes in 236 habitat types. The indicator values presented here are crucial for integrating disturbance niche optima into large-scale vegetation analyses and macroecological studies. Main types of variables contained We set up five main continuous indicator values for European vascular plants: disturbance severity, disturbance frequency, mowing frequency, grazing pressure and soil disturbance. The first two indicators are provided separately for the whole community and for the herb layer. We calculated the values as the average of expert-based estimates of disturbance values in all habitat types where a species occurs, weighted by the number of plots in which the species occurs within a given habitat type. Spatial location and grain Europe. Vegetation plots ranging in size from 1 to 1000 m(2). Time period and grain Vegetation plots mostly sampled between 1956 and 2013 (= 5th and 95th quantiles of the sampling year, respectively). Major taxa and level of measurement Species-level indicator values for vascular plants. Software format csv file
Ellenberg-type indicator values for European vascular plant species
Aims: Ellenberg-type indicator values are expert-based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator-value system proposed by Heinz Ellenberg and co-authors for Central Europe by incorporating other systems of Ellenberg-type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized data set of Ellenberg-type indicator values applicable at the European scale. Methods: We collected European data sets of indicator values for vascular plants and selected 13 data sets that used the nine-, ten- or twelve-degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these data sets. Based on speciesβ co-occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value. Results: We provide a new data set of Ellenberg-type indicator values for 8908 European vascular plant species (8168 for light, 7400 for temperature, 8030 for moisture, 7282 for reaction, 7193 for nutrients, and 7507 for salinity), of which 398 species have been newly assigned to at least one indicator value. Conclusions: The newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large-scale studies of the European flora and vegetation or for gap-filling in regional data sets. The European indicator values and the original and taxonomically harmonized regional data sets of Ellenberg-type indicator values are available in the Supporting Information and the Zenodo repository
- β¦