Moving Beyond Conventional Pressure Diagnostics
The accurate identification of fracture events is one of the most critical — and most challenging — aspects of hydraulic fracturing. Nolte–Smith plots, long the standard for treating pressure interpretation, assume continuous fracture growth from a single initiation point. Field and experimental evidence, however, shows that fracture propagation is intermittent, with alternating periods of dilation, extension, and interaction with natural features.
To capture this complexity, the FracWave Research Group at the University of Houston developed the normalized Continuous Wavelet Transform (CWT) scalogram. Instead of relying on smoothed or averaged pressure curves, the CWT decomposes treating pressure into fine-scale, time–frequency features. The result is a high-resolution map that directly displays fracture propagation signatures as they evolve during pumping.
What the Scalogram Shows
The normalized CWT scalogram turns bottomhole pressure into a color-coded diagnostic that distinctly separates fracture behaviors:
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Green “flames” (normalized coefficients 0.4–0.6): signature of lateral fracture length propagation.
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Red zones (0.8–0.95): indicators of vertical fracture growth or strong fluid diversion into high leak-off intervals.
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Blue areas (0.0–0.25): regions of limited fracture width development, often linked to near-wellbore complexity.
This framework allows fracture propagation modes to be seen directly in treating pressure, without requiring specialized monitoring hardware.
Validation Against Fiber Optics
To verify that these pressure-based signatures represent actual fracture behavior, the method was calibrated against Distributed Fiber Optic Sensing (DFOS) measurements from the Marcellus Shale Energy and Environment Laboratory (MSEEL).
Across multiple stages of the Boggess-5H well, strong alignment was observed:
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Stage 41: Early red-dominated scalogram regions corresponded to limited propagation, matching weak DFOS strain activity. As green patterns emerged, DFOS simultaneously recorded tensile strain bands along the well, confirming fracture tip extension. Later red bursts in the scalogram matched localized DFOS strain spikes, indicating vertical growth or interaction with high leak-off pathways.
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Stage 26: Sustained green scalogram zones reflected dominant lateral propagation across clusters. DFOS strain maps showed consistent extension, while intermittent red spikes coincided with fracture activation events.
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Stage 4: Alternating green and red patterns revealed shifts between fracture length growth and episodes of fluid diversion. DFOS data confirmed these transitions, capturing strain intensifications at the same moments highlighted by the scalogram.
This side-by-side calibration confirmed that fracture propagation signatures are embedded in treating pressure and can be reliably extracted through normalized CWT analysis.
Figures at a Glance
Figure 1. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 26.
Figure 2. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 41.
Figure 3. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 4.
Value to the Industry
The significance of this work lies in its practicality:
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We can now see fracture propagation modes directly on treating pressure — a signal that is already recorded in every frac job.
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No special hardware is required. Unlike microseismic or fiber deployments, this method relies only on standard treating pressure measurements.
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Real-time visibility. Fracture behavior can be tracked while pumping is ongoing.
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Scalable and cost-effective. Applicable to every stage in every well, not just pilots.
By transforming pressure into a diagnostic canvas, the normalized CWT scalogram makes high-resolution fracture diagnostics accessible for routine operations.
Looking Forward
Building on this foundation, the FracWave team has also demonstrated that CWT scalograms can feed machine learning models to predict three-dimensional microseismic event clouds from pressure alone. With validation against both DFOS and microseismic, this creates a path toward fully automated, low-cost fracture monitoring and optimization.
Conclusion
The normalized CWT scalogram reveals what was previously hidden: the dynamic signatures of fracture propagation embedded in treating pressure. Distinct color patterns correspond to lateral extension, vertical growth, and limited width development. These interpretations, validated by fiber-optic strain data from the Marcellus Shale, demonstrate that treating pressure is not just an operational record — it is a powerful real-time diagnostic tool.
Reference
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