|Publisher||Society of Petroleum Engineers||Language||English|
|Content Type||Conference Paper|
|Title||Real-Time Diversion Quantification and Optimization Using DTS|
Gerard Glasbergen, Dan Gualtieri, Rakesh Trehan, and Mary Van Domelen, Halliburton, and Micky Nelson, Occidental of Elk Hills Inc.
SPE Annual Technical Conference and Exhibition, 11-14 November 2007, Anaheim, California, U.S.A.
2007. Society of Petroleum Engineers
|3 Management and Information
3.5 Information Systems and Data Use
3.5.2 Data Integration
In matrix treatments, optimum placement of the injected fluids is essential. Over the years, several diversion techniques have been applied to obtain a desired fluid distribution. The latest developments in the application of distributed temperature sensing (DTS) during matrix treatments to monitor temperature profiles along the wellbore in real time show that fluid distribution can be quantified.
This paper discusses the application of DTS to quantify the effectiveness of diversion agents. Quantification of fluid distribution makes it possible to determine the flow distribution both before and after a diverter stage so that the diversion effect can be evaluated.
Knowledge of the diverter effect will lead to better understanding of the diversion process and subsequently to optimization of future treatment designs. Ultimately, use of real-time quantification of the effect of diversion will lead to the development of real-time optimization itself. In real-time optimization, the results of a diverter stage will be used to adjust the next diverter stage to optimize placement.
The post-treatment analysis of the temperature profiles showed that flow distribution can be quantified both before and after a diverter stage. Based on the observations, the decision was made to develop a diagnostics tool that can be used in real time and will enable real-time quantification.
The novel approach of using the diagnostics tool in combination with DTS during matrix acid treatments will help to further optimize diversion treatments. This optimization is both an optimization during the treatment and an optimization of diverter stages in future treatments.
Optimum fluid placement and complete zonal coverage are essential in a successful matrix acid treatment. This is especially true for long intervals with high degrees of heterogeneity. Without effective fluid diversion, the injected fluids will follow the path of least resistance and will only stimulate strongly depleted zones or the zones with the highest permeability or the least damage. Fluid diversion methods are introduced to divert the flow away from this path of least resistance (Hill 1994; Glasbergen and Buijse 2006). The most commonly used diversion methods are (a) foams, (b) balls, (c) particulates, and (d) gels, or (e) maximizing the injection rates. It is outside the scope of this paper to discuss these diversion methods, which have been discussed extensively in earlier publications (Parlar et al. 1995; Rossen 1994; Erbstoesser 1980; Nitters and Davies 1989; Glasbergen et al. 2006; Lietard 1997; Paccaloni 1995).
Evaluating fluid placement and the effectiveness of fluid diversion has long been recognized as a challenge in the industry. Various techniques have been developed and utilized to determine fluid placement. Example methods include (a) evaluating injection rates along with surface or bottomhole pressures (McLeod and Coulter 1969; Paccaloni 1979; Prouvost and Economides 1989), (b) production logging tools (Glasbergen et al. 2006), (c) radioactive tracers, (d) simulations, and (e) distributed temperature sensing (DTS). In this paper we will limit discussion to the use of DTS.
Distributed Temperature Sensing (DTS)
Oilfield applications using distributed wellbore temperature surveys have been in practice since the early 1950s (Nowak 1953; Kunz and Tixier 1955). Distributed temperature sensing (DTS) is a valuable tool used to understand the dynamics of oil and gas production and injection rates. Some of the earliest work with this technology was to quantify production profiles on producing wells. This is achieved by monitoring the temperature variations caused by flow or injection rates at the reservoir entry points.
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