We will present a methodology that can be used to optimize the completion and stimulation design for horizontal wells. This technique relies on integrating data from all relevant domains to accurately model complex fracture geometries.
The traditional mental model for resource plays envisions resource exploitation as a factory. After the first few wells' worth of " growing pains", where the recipe for future wells is tested and analyzed, each subsequent well of the thousands to come is drilled and completed in exactly the same manner. The unfortunate reality is far different. Though the areal extent of these plays is huge, the consistency of the reservoir response to the same drilling and completion strategies can vary widely between wells only a few thousand feet apart (Jochen et al. 2011). Now even more than ever, the need to integrate subsurface work-streams with the operations has become more apparent. Shales have challenged, repeatedly, the two main assumptions to completion design: that a successful completion is one in which the entirety of the pre-job design was pumped, and that a given design generates a simple geometry.
To deal with the heterogeneity encountered in these formations and the lack of data inherently associated with horizontal wells, it would be advantageous to have a hydraulic fracture model that could be driven from the 3D geologic model and which could capture the complexity often encountered when stimulating these unconventional reservoirs. We will present a methodology in which a single pad was used to calibrate the inputs needed for such a fracturing model, and show how it was able to mimic the variety of fracturing behaviors which were captured via microseismic observation through the integration of geological, petrophysical, and geomechanical data.
The three wells in this study were drilled, completed, and operated under very similar conditions. All three are landed in the lower part of the Marcellus. All three also have approximately 300 foot hydraulic fracturing stages pumped with slickwater and 40/70 mesh proppant with very similar volumes and concentrations of each. Yet, because the response to the same stimulation is very different, it becomes apparent that understanding the variations in properties that influence the stimulations and a fracturing model that can use these inputs is necessary in order to customize the completion to the well.