Eagle Ford Completion Optimization Strategies Using Horizontal Logging Data
Four operators drilling in the Eagle Ford Shale Play located in South Texas, USA joined Schlumberger in an initiative to acquire various types of open hole logging data in several horizontal wells, and then use the data to design the completions with optimum fracture stage and perforation cluster positioning. The wells were then evaluated with horizontal production logs to gauge the effectiveness of using the log data to engineer the completions. This paper will outline the processes used to acquire the data, the analyses made on that data, application, results and conclusion.
The study draws on previous work showing perforation cluster contribution variation in several shale plays including the Eagle Ford (Miller, 2011), and other documented results showing the effect of targeting similarly stressed rock for fracture treatments (Waters, 2011). The main objective was to improve the initial flow capacity of the well by increasing the number of perforation clusters contributing to production. Another related objective was to determine the optimal horizontal logging program that was needed to characterize the rock with minimal interruption to existing work flows. This paper will show the results of data acquired over 12 horizontal wells in the Eagle Ford Shale. Petrophysical and geomechanical analyses were based on horizontal logging measurements and used as inputs to an engineered completion design tool that generated a recommendation on each well design. The design tool grouped intervals with similar properties for stimulation treatment. Following the treatment, horizontal production logs were run through the zones to measure the perforation cluster contribution.
The results of the study have the potential to change the way Unconventional Resources are developed. Recent trends have seen a shift away from data acquisition to blind geometrical fracturing. This paper examines the value of acquiring petrophysical data in the lateral section and its application to completion optimization, the minimization of wasted resources, and the impact on early production.