Due to the shift from conventional reservoirs towards unconventional, ultra-low permeability reservoirs in the last decade, Diagnostic Fracture Injection Test (DFIT) has become one of the dominant and economically practical pressure transient tests. It is crucial to analyze and interpret DFIT data correctly to obtain essential fracture design and reservoir parameters. This study presents the application of wavelet analysis to DFIT falloff pressure data to determine fracture closure pressure and time, to ultimately improve the overall efficiency of hydraulic fracturing designs.

In this study, DFIT pressure is treated as a non-stationary signal and analyzed by one of the signal processing techniques which is wavelet transformation. The purpose of signal analysis is to extract relevant information from a signal by transforming it. Firstly, the signal is transformed into wavelet domain by Discrete Wavelet Transformation (DWT) to calculate high-frequency wavelet coefficients (details), then change-point detection technique is applied to distinguish major changes within the coefficients trend to determine fracture closure pressure and time.

DFIT pressure decline data from different wells were analyzed by wavelet transformation. Detail coefficient demonstrates different patterns depending on the formation analyzed and near wellbore activities. This is expected because wavelet analysis is sensitive to any physical changes within the system. From the amplitude changes of the coefficients, wavelet tool demonstrates the fracture closure as a continuing process.

Because wavelet is sensitive to changes in the system, it detects the fracture closure unambiguously by amplitude change, as compared to slope changes in other conventional methodologies. A comparison with some of the most commonly used diagnostic techniques, conventional log-log diagnostic plot, square root time, G-function and its derivative analysis are also provided in this study.

There have been several publications discussing various techniques analyzing DFIT pressure decline in unconventional formations and yet there is relatively high uncertainty in before-closure-analysis. However, this methodology is more sensitive to fundamental changes in the system, so application in detecting closure pressure and time decreases the uncertainty compared to other conventional tangential methodologies.

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