Treffer: Development of a new analytical model to interpret inter-well poroelastic pressure transient data

Pressure interference data between fractured wells in unconventional formations during fracturing has been shown to yield great insights into the geometry of propagating fractures. Interpretation of this pressure response data can be used to estimate key unknown fracture parameters such as azimuth, height, width, and length. This pressure interference is ascribed to the poroelastic impact of the propagating fracture’s stress shadow. Numerical modeling of these measurements using fully coupled geomechanical simulators has been shown to history match field observations. Numerical modeling, however, can be time-consuming and not feasible for application in on-the-fly solutions during the frac treatment. There is a need for simple, high-speed tools that can guide the staff and engineers on location with insight into the position and geometry of the propagating fractures. This work presents a new analytical model that provides a method for quick analysis of the fracture responses from downhole pressure gauges during treatment, validation with fully 3-D coupled numerical modeling, and successful application to field cases. This new analytical model uses the fundamental stress equations for simple fracture geometries (KGD, PKN, and radial fractures) and captures critical characteristics observed in field pressure interference observations. Multiple field stages with offset pressure responses were captured with bottom-hole gauges, interpreted, and detailed in this body of work. A Python code for these analytical stress predictions was developed to make the process user-friendly and adaptable to ever-changing industry needs. The model closely ties these observed field responses to predicted stresses reported from the model and allows for timely interpretation of the pressure data. To further validate the characteristic stress responses observed in the field, fully coupled 3-D poroelastic simulations were also performed with results showing less than 2% error relative to the analytical predictions. Insights into well ...