Determination of Water Injection Zonal Allocation From Distributed Temperature Sensing Data

2012 ◽  
Author(s):  
Nijat Mehtiyev ◽  
Mahmoodur Rahman ◽  
Dwayne Anthony Bourgoyne
Keyword(s):  
Water Injection ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Sensing Data

scholarly journals Fiber optic distributed temperature sensing for the determination of air temperature

2014 ◽  
Vol 7 (6) ◽  
pp. 6287-6298
Author(s):  
S. A. P. de Jong ◽  
J. D. Slingerland ◽  
N. C. van de Giesen
Keyword(s):  
Solar Radiation ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Fiber Optic ◽  
Solar Heating ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Air Temperatures ◽  
Different Diameters

Abstract. This paper describes a method to correct for the effect of solar radiation in atmospheric Distributed Temperature Sensing (DTS) applications. By using two cables with different diameters, one can determine what temperature a zero diameter cable would have. Such virtual cable would not be affected by solar heating and would take on the temperature of the surrounding air. The results for a pair of black cables and a pair of white cables were very good. The correlations between standard air temperature measurements and air temperatures derived from both colors had a high correlation coefficient (r2 = 0.99). A thin white cable measured temperatures that were close to air temperature. The temperatures were measured along horizontal cables but the results are especially interesting for vertical atmospheric profiling.

Successful Flow Profiling of Gas Wells Using Distributed Temperature Sensing Data

2006 ◽  
Author(s):  
David Oliver Johnson ◽  
Jose R. Sierra ◽  
Jiten D. Kaura ◽  
Dan Gualtieri
Keyword(s):  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Gas Wells ◽  
Sensing Data

scholarly journals Fiber optic distributed temperature sensing for the determination of the nocturnal atmospheric boundary layer height

2011 ◽  
Vol 4 (2) ◽  
pp. 143-149 ◽  
Author(s):  
C. A. Keller ◽  
H. Huwald ◽  
M. K. Vollmer ◽  
A. Wenger ◽  
M. Hill ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Fiber Optic ◽  
Temperature Inversion ◽  
Nocturnal Boundary Layer ◽  
Temperature Sensing ◽  
Temperature Profiles ◽  
Distributed Temperature Sensing ◽  
Boundary Layer Height

Abstract. A new method for measuring air temperature profiles in the atmospheric boundary layer at high spatial and temporal resolution is presented. The measurements are based on Raman scattering distributed temperature sensing (DTS) with a fiber optic cable attached to a tethered balloon. These data were used to estimate the height of the stable nocturnal boundary layer. The experiment was successfully deployed during a two-day campaign in September 2009, providing evidence that DTS is well suited for this atmospheric application. Observed stable temperature profiles exhibit an exponential shape confirming similarity concepts of the temperature inversion close to the surface. The atmospheric mixing height (MH) was estimated to vary between 5 m and 50 m as a result of the nocturnal boundary layer evolution. This value is in good agreement with the MH derived from concurrent Radon-222 (222Rn) measurements and in previous studies.

scholarly journals Fiber optic distributed temperature sensing for the determination of the nocturnal atmospheric boundary layer height

2010 ◽  
Vol 3 (3) ◽  
pp. 2723-2741 ◽  
Author(s):  
C. A. Keller ◽  
H. Huwald ◽  
M. K. Vollmer ◽  
A. Wenger ◽  
M. Hill ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Fiber Optic ◽  
Temperature Inversion ◽  
Nocturnal Boundary Layer ◽  
Temperature Sensing ◽  
Temperature Profiles ◽  
Distributed Temperature Sensing ◽  
Boundary Layer Height

Abstract. A new method for measuring air temperature profiles in the atmospheric boundary layer at high spatial and temporal resolution is presented. The measurements are based on Raman scattering distributed temperature sensing (DTS) with a fiber optic cable attached to a tethered balloon. These data were used to estimate the height of the stable nocturnal boundary layer. The experiment was successfully deployed during a two-day campaign in September 2009, providing evidence that DTS is well suited for this atmospheric application. Observed stable temperature profiles exhibit an exponential shape confirming similarity concepts of the temperature inversion close to the surface. The atmospheric mixing height (MH) was estimated to vary between 5 m and 50 m as a result of the nocturnal boundary layer evolution. This value is in good agreement to the MH derived from concurrent Radon-222 (222Rn) measurements and in previous studies.

Value of DTS in Multi-Stacked Reservoirs to Better Understand Injectivity and Water Flood Effectiveness – A Field Example from the UAE

2021 ◽  
Author(s):  
Jorge Gomes ◽  
Jane Mason ◽  
Graham Edmonstone
Keyword(s):  
Lower Cretaceous ◽  
Fiber Optic ◽  
Water Movement ◽  
Water Injection ◽  
Reservoir Management ◽  
Oil Production ◽  
Temperature Sensing ◽  
Dense Layer ◽  
Distributed Temperature Sensing ◽  
Injection Water

This paper highlights the application of downhole fiber optic (FO) distributed temperature sensing (DTS) measurements for well and reservoir management applications: 1) Wellbore water injectivity profiling. 2) Mapping of injection water movement in an underlying reservoir. The U.A.E. field in question is an elongated anticline containing several stacked carbonate oil bearing reservoirs (Figure 1). Reservoir A, where two DTS monitored, peripheral horizontal water injectors (Y-1 and Y-2) were drilled, is less developed and tighter than the immediately underlying, more prolific Reservoir B with 40 years of oil production and water injection history. Reservoirs A and B are of Lower Cretaceous age, limestone fabrics made up of several 4th order cycles, subdivided by several thin intra dense, 2-5 ft thick stylolitic intervals within the reservoir zones. Between Reservoir A and Reservoir B there is a dense limestone interval (30-50 ft), referred as dense layer in the Figure 1 well sections.

scholarly journals Fiber optic distributed temperature sensing for the determination of air temperature

2015 ◽  
Vol 8 (1) ◽  
pp. 335-339 ◽  
Author(s):  
S. A. P. de Jong ◽  
J. D. Slingerland ◽  
N. C. van de Giesen
Keyword(s):  
Air Temperature ◽  
Urban Areas ◽  
Fiber Optic ◽  
Solar Heating ◽  
Temperature Sensing ◽  
Temperature Measurements ◽  
Distributed Temperature Sensing ◽  
Air Temperatures ◽  
Different Diameters

Abstract. This paper describes a method to correct for the effect of solar radiation in atmospheric distributed temperature sensing (DTS) applications. By using two cables with different diameters, one can determine what temperature a zero diameter cable would have. Such a virtual cable would not be affected by solar heating and would take on the temperature of the surrounding air. With two unshielded cable pairs, one black pair and one white pair, good results were obtained given the general consensus that shielding is needed to avoid radiation errors (WMO, 2010). The correlations between standard air temperature measurements and air temperatures derived from both cables of colors had a high correlation coefficient (r2=0.99) and a RMSE of 0.38 °C, compared to a RMSE of 2.40 °C for a 3.0 mm uncorrected black cable. A thin white cable measured temperatures that were close to air temperature measured with a nearby shielded thermometer (RMSE of 0.61 °C). The temperatures were measured along horizontal cables with an eye to temperature measurements in urban areas, but the same method can be applied to any atmospheric DTS measurements, and for profile measurements along towers or with balloons and quadcopters.

scholarly journals Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

2016 ◽  
Vol 52 (10) ◽  
pp. 7690-7710 ◽  
Author(s):  
Jianzhi Dong ◽  
Susan C. Steele-Dunne ◽  
Tyson E. Ochsner ◽  
Christine E. Hatch ◽  
Chadi Sayde ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Temperature Sensing ◽  
Distributed Temperature Sensing ◽  
Sensing Data
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