Modeling Flow Profile using Distributed Temperature Sensor (DTS) System
- Xiaowei Wang (Baker Hughes) | Jaedong Lee (Baker Hughes) | Brian Thigpen (Baker Hughes) | Guy Paul Vachon (Baker Hughes) | Stephen H. Poland (Baker Hughes) | Douglas Norton (Baker Hughes Production Quest)
- Document ID
- Society of Petroleum Engineers
- Intelligent Energy Conference and Exhibition, 25-27 February, Amsterdam, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2008. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 1.6.9 Coring, Fishing, 5.2.1 Phase Behavior and PVT Measurements, 5.6.11 Reservoir monitoring with permanent sensors, 5.9.2 Geothermal Resources, 3.3.1 Production Logging, 5.3.2 Multiphase Flow, 3.2.2 Downhole intervention and remediation (including wireline and coiled tubing), 2.2.2 Perforating, 5.4.6 Thermal Methods, 5.5 Reservoir Simulation, 2 Well Completion
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Distributed Temperature Sensing (DTS) technology uses fiber-optic cable to measure continuous temperature profile along the wellbore. Measurement interpretation can provide valuable information, and one of them is real time flow profiling that helps to monitor the fluid flow in wells. This valuable information can assist real time production decision with no well intervention. However, the complexity of the data analysis limits the use of DTS as a flow allocation technique.
This paper presents a new flow-profiling model using DTS technology. The model is based on steady-state energy balance equation and it handles multiple production zones with its own zonal fluid properties. The model is applicable for gas and oil wells in onshore and offshore environment. The model is integrated into easy-to-use software and it can be run in two modes: forward simulation and flow profiling. The forward simulation calculates temperature distribution along the wellbore for any given production profile, and this mode is critical for the model calibration. It is also very useful for emulating what-if scenarios, like water breakthrough. The flow profiling estimates production profile based on measured temperatures, which is the base for the real time well monitoring.
Our studies with the model show that geothermal profile, fluid properties, formation properties, well completion, and deviation as well as Joule-Thomson effect all play key roles for the model accuracy. Joule Thomson gas cooling effect only occurs at lower pressure while reversal appears at higher pressure region.
The model is tested against synthetic, literature and field examples and good agreements have been obtained. Test results have been presented.
Distributed Temperature Sensor (DTS) is the name of the class of instruments that measure temperature continuously through the optic fiber installed along the entire wellbore length. DTS comprises concentric layers of materials: core and cladding. DTS uses physical phenomena such as Raman scattering which transduces temperature into an optical signal. Laser light pulses are generated by the DTS instrument (DTS box) and launched down the fiber sensor. As laser pulses travel down, portion of the light reflects back to the DTS box. Raman backscatter is caused by molecular vibration in the fiber resulting in the emission of photons, which are shifted in wavelength from the incident light1. Positively shifted Stokes backscatter is temperature independent, while the negatively shifted Anti-Stokes Raman backscatter is temperature dependent. The intensity ratio of Stocks/Anti-Stokes can be used to calculate temperature.
DTS technology is not new. It was used in fire detection decades ago. Only in recent years, DTS technology has emerged as a valuable tool in the oil and gas industry. Initial applications are for steam flooding and geothermal application. As DTS technology advances, the temperature measurement has become very accurate and reliable. The temporal temperature resolution is 0.1oC at a distance up to 10 km, with a spatial resolution of 2 meters. DTS system generally don't interfere with flow, have much more flexibility for deployment in restricted downhole environments, and can be used for short-term as well as permanent monitoring scenarios.
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