Diagnosing Multi-Cluster Fracture Propagation Using Dynamic Poroelastic Pressure Transient Analysis

Seth, Puneet (The University of Texas at Austin) | Manchanda, Ripudaman (The University of Texas at Austin) | Elliott, Brendan (Devon Energy) | Zheng, Shuang (The University of Texas at Austin) | Sharma, Mukul (The University of Texas at Austin)



During stimulation in a treatment well, offset well pressure measurements resulting from stress-shadow related interference are often used to estimate hydraulic fracture geometry. Current pressure interference models typically assume one dominant fracture per stage in their analysis, which is an overly simple assumption and can result in erroneous estimates of hydraulic fracture geometry. This stems from the limited capability of existing models which are not equipped to interpret multi-cluster fracture propagation scenarios. In this study, we present workflows to analyze dynamic pressure time-series responses observed at offset monitor wells during injection in a nearby treatment well, to diagnose multi-cluster fracture propagation.

A fully-coupled, 3-D, reservoir-fracturing simulator which models hydraulic fractures explicitly as compliant discontinuities has been used to simulate pressure interference in multi-well pads. We model dynamic fracture propagation from multiple clusters in the treatment well and analyze the corresponding pressure changes observed at an offset monitor well. We apply our dynamic pressure transient analysis model to analyze multi-cluster fracture propagation from the treatment well and contrast it with a scenario that assumes one dominant fracture per stage. We show that the simulated offset pressure response during the propagation of one dominant fracture per stage is very different compared to a multi-cluster propagation.

We analyze the dynamic intra-stage offset well pressure signatures (inflections in the pressure response, slope of the pressure response, arrival times etc.) to develop workflows to test diversion effectiveness and provide insights on optimum job volumes, in a relatively inexpensive manner. We show the impact of completion design and perforation erosion on the offset well pressure response. We test different cluster designs and analyze the offset well pressure response in each case to quantify cluster efficiency. We apply our dynamic pressure interference testing model to field data from the Permian Basin to test for diversion effectiveness and diagnose dominant cluster variability during stimulation.