Fast Analysis Method of Diagnostic Fracture Injection Test Data: Examples from Four Different Shale Formations (Bakken, Three Forks, Wolfcamp, and Eagle Ford)

This study introduces a new methodology for analyzing DFIT data by applying Gaussian Pressure Transient (GPT) solutions to model pressure fall-off behavior in hydraulically fractured wells. Diagnostic Fracture Injection Test (DFIT) data from 7 wells were analyzed; the wells were drilled in four different shale formations (Wolfcamp, Eagle Ford, Bakken, and Three Forks). All wells analyzed were hydraulically fractured, and as a first step in the fracturing treatment operation, each of the completion companies involved in the different wells applied a DFIT test to determine the ISIP and the formation leak-off rate to estimate the permeability. The original pressure fall-off excel sheets, availed by different operators, were analyzed in detail. For each test, the relevant part of the leak-off test data was identified and isolated for further in-depth study of the leak-off rate. Instead of relying on traditional DFIT interpretation techniques such as G-function or Carter leak-off assumptions, the method leverages the Gaussian diffusion model to capture the pressure propagation and leak-off characteristics more accurately. The Gaussian pressure transient analysis combines pressure and volume balancing principles directly on the actual field measurements. The results show that the Gaussian method successfully matched the pressure fall-off curves for all 7 wells, with improved accuracy in estimating leak-off rates and hydraulic diffusivity. Moreover, the GPT method requires only a short-term pressure fall-off curve to accurately determine reservoir properties, and this can significantly shorten the typical long fall-of time in low permeability rocks. The Gaussian pressure model provided excellent matches with field data, confirming the method's reliability for unconventional reservoir analysis. The proposed methodology thus offers significant advancement over conventional techniques, better aligning DFIT analysis with the complex realities of unconventional reservoirs.