[Objective] The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation. [Method] The characteristics, getting manner and displaying method of L-band soundin...[Objective] The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation. [Method] The characteristics, getting manner and displaying method of L-band sounding seconds data were introduced briefly. Moreover, its application prospect in the artificial precipitation operation was analyzed initially. We aimed to improve its application rate in the artificial precipitation operation. [Result] L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data, and the precision reached the second level. It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field, and improve the numerical model. Moreover, the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool, the determination of operation height and range, and guide the artificial precipitation operation, and improve the operation efficiency. [Conclusion] The research provided the analysis and reference basis for the command of artificial precipitation operation.展开更多
The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pr...The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data.The initial flowback involves producing back the fracturing fuid after hydraulic fracturing,while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells.The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes.Such a comparison is useful for evaluating well performance and optimizing frac-turing operations.We construct rate-normalized pressure(RNP)versus material balance time(MBT)diagnostic plots using both initial and second flowback data(FB;and FBs,respectively)of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin.In general,the slope of RNP plot during the FB,period is higher than that during the FB;period,indicating a potential loss of fracture volume from the FB;to the FB,period.We estimate the changes in effective fracture volume(Ver)by analyzing the changes in the RNP slope and total compressibility between these two flowback periods.Ver during FB,is in general 3%-45%lower than that during FB:.We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index(CDI),hydrocarbon-drive index(HDI),and water-drive index(WDI).The dominant drive mechanism during both flowback periods is CDI,but its contribution is reduced by 16%in the FB,period.This drop is generally compensated by a relatively higher HDI during this period.The loss of effective fracture volume might be attributed to the pressure depletion in fractures,which occurs during the production period and can extend 800 days.展开更多
SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive...SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive upgrades across industries through embedded intelligence,ushering in an era of intelligent interconnection of all things.However,managing real-time interactions among devices,infrastructure,and services in 6G networks is much more complex than in previous generations.Massive data streams from terrestrial nodes(e.g.,edge devices,sensors,distributed computing)and non-terrestrial nodes(LEO/MEO/GEO satellites)demand more intelligent and efficient processing.展开更多
文摘[Objective] The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation. [Method] The characteristics, getting manner and displaying method of L-band sounding seconds data were introduced briefly. Moreover, its application prospect in the artificial precipitation operation was analyzed initially. We aimed to improve its application rate in the artificial precipitation operation. [Result] L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data, and the precision reached the second level. It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field, and improve the numerical model. Moreover, the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool, the determination of operation height and range, and guide the artificial precipitation operation, and improve the operation efficiency. [Conclusion] The research provided the analysis and reference basis for the command of artificial precipitation operation.
文摘The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data.The initial flowback involves producing back the fracturing fuid after hydraulic fracturing,while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells.The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes.Such a comparison is useful for evaluating well performance and optimizing frac-turing operations.We construct rate-normalized pressure(RNP)versus material balance time(MBT)diagnostic plots using both initial and second flowback data(FB;and FBs,respectively)of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin.In general,the slope of RNP plot during the FB,period is higher than that during the FB;period,indicating a potential loss of fracture volume from the FB;to the FB,period.We estimate the changes in effective fracture volume(Ver)by analyzing the changes in the RNP slope and total compressibility between these two flowback periods.Ver during FB,is in general 3%-45%lower than that during FB:.We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index(CDI),hydrocarbon-drive index(HDI),and water-drive index(WDI).The dominant drive mechanism during both flowback periods is CDI,but its contribution is reduced by 16%in the FB,period.This drop is generally compensated by a relatively higher HDI during this period.The loss of effective fracture volume might be attributed to the pressure depletion in fractures,which occurs during the production period and can extend 800 days.
文摘SI:Agentic AI for 6G Networks.Introduction.6G networks are poised to provide full coverage across air,land,and sea,deliver terabit-per-second data rates,and achieve microsecond-level latency.They promise comprehensive upgrades across industries through embedded intelligence,ushering in an era of intelligent interconnection of all things.However,managing real-time interactions among devices,infrastructure,and services in 6G networks is much more complex than in previous generations.Massive data streams from terrestrial nodes(e.g.,edge devices,sensors,distributed computing)and non-terrestrial nodes(LEO/MEO/GEO satellites)demand more intelligent and efficient processing.
