Cluster Flow Identification During Multi-Rate Testing Using a Wireline Tractor Conveyed Distributed Fiber Optic Sensing System With Engineered Fiber on a HPHT Horizontal Unconventional Gas Producer in the Liard Basin

Abstract

Abstract This paper investigates the flow performance of a horizontal unconventional gas producer where cluster flow has been detected and quantified. The method employed is the first use of a wireline deployed engineered fiber optic Distributed Acoustic Sensing (DAS) system for the purpose of production monitoring. It further describes the technological advancement of the DAS system. While distributed fiber optics sensors have been used in unconventional wells for over ten years, the focus has been on stimulation monitoring, where relatively large thermal and acoustic signatures are easily measured. There has been limited success on the use of the same systems for production monitoring in horizontal wells since the thermal and acoustic flow signatures are very small. The recent technological advancements achieved in the area of Distributed Acoustic Sensing, using engineered fibers with dramatic improvements in the signal-to noise ratio, now make it possible to sense low levels of inflow noise with DAS systems. This study presents two distinct ways of analysing DAS data to extract production information and demonstrates the analysis using real field data. The well is an unconventional dry gas producer in the Liard Basin, Canada, which was completed in 18 stages, with 3 cluster per stage, a total of 54 clusters along a horizontal section of ~2000m. The top 11 stages were logged on tractor conveyed wireline with DAS and DTS data acquired over a 19-hour period. The acquisition covered an initial shut-in period, followed by a ramp up to a maximum test rate, and a final shut-in period. Data from the full acquisition period was analysed and is presented in the paper. The data acquired at various points during the acquisition period was used to detect and quantify inflow. It was found that one of the clusters was active throughout the shut-in period and inactive during the maximum test rate. We were also able to identify more clusters being activated as the production rate increased. The increase in the number of active clusters was not proportional to the increase in the surface gas rate, with heel side clusters becoming active before the toe side clusters as the production rate was increased. The results of this survey yielded remarkable insights that informed the operator on the completion efficiency and the relationship between the total production rate and cluster activity.