Although closure detection has a crucial role in hydraulic fracturing operations, significant debate surrounds the various methodologies to determine its value. Several competing methodologies have been presented in the literature that sometimesyield significantly different estimates of closure pressure and time. The conventional techniques rely on assumptions that may be competing or even contradictory.
The continuous wavelets transform technique is a data transform technique that convolves the pressure and/or temperature data using a short wavy signal called "wavelet". The wavelet transform provides a representation of the pressure signal by letting the translation and scale parameters of the wavelets vary continuously. That enables the analyst to find the details of the pressure data by observing the wavelet energy spectrum for the monitored signal (pressure and/or temperature) signal. In this case the event of contact between two fracture faces and complete fracture closure is clearly identified.
As a part of The EGS Collab project, a series of fracture injection tests have been conducted to estimate the minimum principal stress with direct observation of well bore deformation using the SIMFIP tool (Step-Rate Injection Method for Fracture In-Situ Properties). The tool monitors the deformation using strain gauges as a fracture opens and closes during multiple tests. The publicly available data provide a great opportunity to experimentally calibrate the new technique for detecting the closure event using continuous wavelet transform. The effect of fracture closure events and fracture faces contact events detected using continuous wavelet transform were compared to the experimental measured deformation.
The continuous wavelet transform technique for closure detection showed an agreement with the deformation measurement. The effect of the presence of natural fractures and complex fracture closure events were recognized using the continuous wavelet transform technique. The Contineous Wavelet Transform (CWT) is a global technique that can be applied to the pressure decline data without requiring further information about the reservoir geomechanical parameters or pumping data. The technique can be easily embedded in machine learning algorithms for hydraulic fracturing diagnostics.