Stage length and perforation cluster spacing are important design parameters for multi-stage hydraulic fracturing. This study aims to demonstrate that the interplay between subtle variations of the least principal stress (Shmin) with depth and the stress shadows induced by simultaneously propagating hydraulic fractures from multiple perforation clusters, primarily determines the propped and fractured area in the target formations. This principle is illustrated with the help of a case study in a prolific unconventional formation in the north eastern US, where the vertical stress variations are well characterized through discrete multi-depth stress measurements and actual stage design parameters used by the operator are known. At first, we show how the hydraulic fracture footprint and proppant distribution varies with a change in the vertical stress profile. The stress profile is shown to be a very important in determining the optimal vertical and lateral well spacing. The evolution of the stress shadow in the different layers is shown during the pumping as the fracture propagates across multiple layer boundaries. Subsequently, we demonstrate that by changing the magnitude of stress perturbations caused by the stress shadow effect, the distribution of propped area can be altered significantly. We use this method to determine the optimal cluster spacing keeping other design parameters constant such as flow rate, perforation diameter, etc. Simulations from selected cluster spacing realizations are run with high and low permeability scenarios to show the importance of correct matrix permeability inputs in determining the three-dimensional depletion profile and ultimate production. By varying the cluster spacing we show the hydraulic fracture propagation change from being solely stress layering driven to stress shadow influenced. The effect of stress shadow on the final fracture footprint is highly specific depending on the given stress layering and is thus case-dependent. This study demonstrates that knowledge of stress variations with depth and modeling are critical for optimizing stimulation efficiency.
