Summary

Low-frequency distributed acoustic sensing (LF-DAS) data are a powerful attribute to detect fracture hits and characterize fracture geometry during multistage hydraulic fracturing treatments in unconventional reservoirs. The DAS data in low-frequency bands linearly correlate with strain and strain rate induced by dynamic fracture propagation. Due to the complexity of multiple-fracture propagation in unconventional reservoirs, the measured signals from different wells exhibit various characteristics. Mechanisms causing the differences are not well understood, which makes the interpretation of real LF-DAS data and detection of fracture hits very challenging. Hence, it is necessary to relate the observed strain/strain-rate signatures to specific fracture patterns based on the physical model of rock deformation during fracture propagation and to quantitatively characterize signatures surrounding fracture hits. In this study, we have applied our in-house fracture propagation model to simulate simultaneous multiple-fracture propagation as well as fracture-induced strain and strain-rate responses along an offset monitor well. Then a general guideline for fracture-hit detection is proposed based on quantitative analysis of strain/strain-rate responses during multiple-fracture propagation. Finally, a set of field examples are presented to demonstrate the potential of LF-DAS data on hydraulic fracturing monitoring.

During multiple-fracture propagation, a “heart-shaped” zone with positive strain rates may be identified for each fracture before the fracture hit. Immediately after the fracture encounters the monitor well, part of the fiber within the fracture path keeps being extended, while the fiber sections off the path become compressed. Three 1D features along the channel (location) axis are designed to detect fracture hits. The features are maximum strain rate, the summation of strain rates, and summation of strain-rate amplitudes. Channels with fracture hits usually exhibit significant peak values of the three features. However, the characteristic signatures can be less detectable when the gauge length is close to the cluster spacing. Connections between fracture-hit locations and cluster perforations clearly reflect the fracture propagation direction. The field examples illustrate the complexity of real LF-DAS signals and demonstrate the adequacy of the proposed guideline for fracture-hit detection with multicluster completion. The fractures propagate nearly perpendicular to the horizontal wellbore in this unconventional shale formation. In addition, four to five fractures out of eight perforation clusters can propagate 396.24 m (1,300 ft) and hit the monitor well, and the “heel-biased” fracture pattern is observed (fractures that do not hit the monitor well are usually close to the toe side). Fracturing fluid leaking off into the previous stage can also be diagnosed, which could negatively affect the completion efficiency.

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