Horizontal drilling and multi-stage hydraulic fracturing are the key technologies enabling the oil and gas industry to unlock unconventional resources. Based on microseismic data, hydraulic fracturing in shale reservoirs typically creates highly complex fracture networks due to their complex geology and the activation of the pre-existing natural fractures that cannot be realistically captured when the classical planar bi-wing fracture models are implemented. Coupling geomechanical modeling with microseismic data interpretation helps a better understanding of the fracture complexity that is anticipated for enhanced production performance. Recently, new stimulation design patterns have been proposed for production optimization using this approach by coupling the fundamental geomechanical concepts in order to waive the stress interference constraint of the minimum fracture spacing and to produce further fracture complexity in the altered stress regions.
The goal of this study was to evaluate and compare two new stimulation patterns, namely, the alternate fracturing and the zipper fracturing, to the consecutive multi-stage stimulation pattern from fluid flow perspective using a numerical dual-permeability model. The study considers anticipated fracture complexity and stimulated reservoir volume (SRV) overlap of parallel lateral wells. An economic evaluation was also conducted to couple the flowbased optimization with the net present value (NPV) analysis to quantify costs vs. benefits of selecting a specific stimulation pattern.
Results of this research demonstrate how the configuration of fracture stages, complexity of fracture network and SRV overlap impact the production performance and eventually NPV of the studied stimulation patterns. The comparison study highlights the governing parameters and provides insights on the significance of developing and optimizing a stimulation pattern utilizing fluid flow evaluation. A hybrid alternate-zipper pattern was also suggested in this paper to take advantage of both patterns by combining the anticipated fracture complexity and SRV overlapping features.