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. Introduction 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 0.1°C 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.

Recently, the industry has seen an enormous increase in the amount of upstream data delivered with fine resolution and accuracy as provided by downhole monitoring equipment. Downhole measurements include distributed temperatures along the wellbore, wellbore pressure and temperature at discrete points, zonal flow rates, equipment performance data such as ESP operating efficiency, and downhole and surface chemical injection data. Even though the potential benefits of these measurements are recognized, practical models and processes that take full advantage of the actual data have not been well established. Intelligent wells are gaining momentum in the oil and gas industry for production optimization, but utilization of this technology is limited to a single well or small group of wells, addressing somewhat localized optimization. Ultimate production optimization achieves higher reservoir recovery through incremental hydrocarbon production, and it needs a higher view than a well-centric approach. Considering that multiple intelligent wells comprise an intelligent field, all the data coming from each intelligent well should be brought together as input for a global optimization. This is analogous to bringing a fuzzy picture of a puzzle into focus as all the pieces fall into place. Similarly, to optimize a 5-spot waterflood, each well in the pattern should be intelligently controlled all the time and, in addition, they need to be optimized relative to each other so that the flood front movement is managed for maximum sweep efficiency. Optimization at this level is accomplished through an integrated use of reservoir/well modeling and real time data acquired through continuous downhole measurements. Measured data enables active model tuning, which in turn improves ongoing reservoir performance prediction. Sensitivity analyses find the optimal configuration of the intelligent wellbore components in injection and production wells that enables active waterflood front control. This paper discusses methodologies used for the control mechanism, including a simplified reservoir model, continuous monitoring data, and a multi-well optimization process. Visualization and control of water flood efficiency, continuous tuning of the reservoir/well model, improved performance prediction and full utilization of real time data are some of the benefits from the process developed. Introduction If one were to search for publications on Smart Fields, I-Fields, Digital Oil Fields, Fields of the Future and other catch words, surely many would cover the same ground as ours with a few exceptions (Vachon et al. 2006, Saputelli et al. 2003, Sarma et al. 2005, Purves et al. 1997, Hardy et al. 1982, Going et al. 2006, Oberwinkler et al. 2004, Brouwer et al. 2004, Oberwinkler et al. 2005, Silin et al. 2005). This paper describes some of the methodologies we used and workflows which are associated with modeling, monitoring, optimizing and subsequent control of the various surface and downhole controls available in this system. Our approach goes both one step deeper by showing the exact workflows' details and also one step back by avoiding unnecessary confusion with complex software design principles. We will first describe our system approach for this inverted 5-spot water flood as it pertains to overall objectives and how the hardware system, its constraints and monitoring thereof, were used to provide relevant data based on our assumptions. Next we will describe the modeling approach and how we are performing the system optimization. Following this we will discuss the workflows that were created to capture the movement of data and information that is used for active water flood management as it pertains to several types of scenarios that are likely to occur during the lifetime of the system. These include a high level generic production workflow followed by several specific examples such as active water management and how the system reacts to an unplanned shut-in.