A Case Study of Completion Effectiveness in the Eagle Ford Shale Using DAS/DTS Observations and Hydraulic Fracture Modeling

Abstract

The objective of this study was to evaluate treatment distribution and fracture geometry in a multi-stage, multi-cluster fracture completion performed in a horizontal Eagle Ford well. Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) data were acquired on the subject well. The DAS/DTS-observed fracture treatment distributions were then modeled in a three-dimensional fracture model in an effort to visually represent resultant fracture geometries. This process was used to evaluate the impacts on the resulting treatment distributions that occurred as a result of stress-shadowing between fractures. The ultimate goal was to understand the influence that adjacent fractures within a stage and adjacent stages have on fracture distribution, fracture geometry, and completion effectiveness. DAS/DTS data suggest a high level of interference between adjacent fractures. Interference between adjacent fractures within a given stage, and from adjacent fracture stages, results in a consistent geometric predominance for fracture growth in the most heel-ward perforation cluster. DAS/DTS results also indicate that an excessive number of perforation clusters, spaced closely together, magnify the negative effects of stress shadowing, and potentially diminish completion effectiveness. Operationally, the DAS/DTS data showed that the surface pressure response originally attributed to downhole diversion from particulate diverters was in fact not due to diversion. Once a dominate fracture was established in a given stage, it remained dominate throughout the entire stage even though two diverter drops per stage were incorporated into the treatment. Finally, the DAS/DTS data indicated that a significant portion (71%) of the stages experienced intra-stage communication. The large majority of this communication was due to plug leakage.

Discrete fracture networks modeling of shale gas production and revisit rate transient analysis in heterogeneous fractured reservoirs

“…(Hunt et al 2009). Microseismic data can be used to calibrate fracture networks and constrain fracture dimensions (Clarkson 2011;Zhou et al 2016), individual stage fracture effectiveness can be assessed with production logging, distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data (Pang et al 2016;Wheaton et al 2016). The use of proppant tracer followed by spectral gamma ray logging can help identify the unstimulated area in a horizontal wellbore (Leonard 2016).…”

Wellbore Cool Down During Hydraulic Fracturing Simplifies Using Dissolvable Materials

“…Similarly, distributed fibre optics has been used to describe the cooling effects during hydraulic fracturing [10,11]. Most of these temperature diagnostics have been used to determine how the formation is accepting the injected fluids [12]. During the fracturing operation, the wellbore cool down has implications on the design and expected performance operation of dissolvable tools.…”

Laboratory Experiments Contrasting Growth of Uniformly and Nonuniformly Spaced Hydraulic Fractures

“…(2014) and Wheaton et al. (2016), indicate that one or two clusters dominate and take most of the fracturing fluid/proppant volume. Finally, Ugueto et al.…”

“…Furthermore, Wheaton et al. (2016) investigated the effect of spacing on cluster efficiency (i.e., proportion of clusters that are substantially producing). In their study, several stages had perforation clusters which were spaced nonuniformly, approximately two times the distance between the other perforation clusters in the stage.…”

“…Early micro-seismic evidence and model-based discussion of this phenomenon is available in the Barnett Shale (Fisher et al, 2004), with more recent evidence from production logs in the Marcellus Shale (Bunger & Cardella, 2015). Furthermore, Wheaton et al (2016) investigated the effect of spacing on cluster efficiency (i.e., proportion of clusters that are substantially producing). In their study, several stages had perforation clusters which were spaced nonuniformly, approximately two times the distance between the other perforation clusters in the stage.…”