Abstract
Abstract
Distributed Temperature Sensing (DTS) is an emerging technology which allows temperature measurement and transmittal along the entire length of the well. An enclosed fiber-optic cable is deployed into the well to allow a continuous, real-time snapshot of the well's temperature profile. Since its introduction in 1995, DTS has had an increasing number of applications including determining production and injection profiles, monitoring stimulation operations, and performing gaslift surveys. This paper discusses DTS as a method for downhole leak detection based on a North Slope field trial conducted between 2004–2006. Twelve operations were performed using two different vendors' retrievable DTS systems, successfully identifying tubing, production casing, sand plug, and packer leaks. Theory, case histories, and future improvements are summarized in this paper. Additionally, a comparison between conventional leak detection logs is presented along with the advantages and disadvantages of DTS surveys.
Introduction
Prudhoe Bay, Alaska, located on the North Slope is a mature enhanced oil recovery/waterflood oil field which has experienced well integrity issues as the field ages. Conventional leak detection logs (LDL's) are commonly performed as an operational diagnostic tool when annular communication is detected. In 2004, a field trial using temporary, slickline- and wireline-conveyed DTS was initiated to determine if the system could be used as a reliable leak detection method.
Conventional leak detection diagnostics. Conventional LDL's are typically performed on wireline at Prudhoe Bay. The toolstring includes temperature, pressure, and casing collar locator (CCL) sondes. If the leak is in the tubing and is large enough, a spinner and density sonde may be included as well.
With the well shut in, a thermal baseline pass is logged from surface to below the packer, typically at 150 feet per minute (fpm). A second logging pass is then completed while pumping into either the tubing or the "A" annulus (Figure 1), depending on the size and location of the leak. Repeat passes of logged anomalies are performed to ensure accurate leak identification, often at slower logging speeds.
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