A novel DFIT simulator comprising a 3D hydraulic fracturing model seamlessly coupled within one software with reservoir flow and geomechanical modeling is described and used to numerically analyze DFITs in unconventional reservoirs. This workflow involves history matching treatment or injection pressures (fracture propagation) and shut-in (fracture closure) pressures consistent with 3D growth of hydraulic fractures in the presence of pressure dependent leak-off. These are the same fundamental processes which characterize Dynamic Stimulated Reservoir Volume or DSRV growth (Sen et al., 2018, Min et al., 2018) and DFITs can therefore be used to get a better early prognosis on the potential of DSRV growth in a tight reservoir.

This modular DFIT simulator iteratively couples a finite-difference reservoir simulation with a finite- element geomechanical modeling within one software and can therefore maintain important consistencies between fracture opening, propagation, closure and the stress dependent leak-off and permeability evolution inside the induced dynamic SRV. Both DFIT injection and closure processes are numerically modeled - and depending on which model parameters we choose to fix and which we perturb, we can preemptively estimate the potential for a successful stimulation and its possible dimensions.

This estimate can be obtained at the early stages of a field /section development, before embarking on major drilling and completion campaigns, even in the absence of substantial production data. And it provides guidance for optimizing major fracturing design and well spacing.

This approach is not reliant or bound by the assumptions underlying widely-used analytical DFIT analyzing methods, and is therefore more flexible and better captures the physics of stimulation in unconventional reservoirs. An early understanding of the key geomechanical metrics defining unconventional reservoir enhancement (DSRV effectiveness) allows us to build a directional relationship between fracturing parameters and post-fracture production without the need for an extended record of production trends. This speeds up the continuous learning and adaptive process of completion optimization involving pumped volumes, cluster spacing and well landing zones.

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