In multi-stacked unconventional development, optimization of completion and well spacing is a major challenge. Because of complex interactions between tightly placed wells in different target zones, it is necessary to model the interference between the wells together with depletion and flow across zones. This integrated scenario modeling is a pre-requisite for full field development and we have developed a new workflow combining physics-based geomechanical reservoir simulation and data-driven analytics. Using the hybrid method, we can efficiently identify key drivers for completion and well spacing optimization. Our method involves statistically selecting multiple single wells from our portfolio, using them in Dynamic Stimulated Reservoir (DSRV) Modeling (using geomechanically influenced permeabilities during fracturing and production) to build prediction functions, and then validating them against actual multi-well field data. For field development scenarios of multi-stacked reservoirs, we applied Design of Experiment (DoE) method and Multivariate analysis (MVA) to screen and rank scenarios. For completion optimization, multiple production profiles predicted from simulated cases were used to generate response surfaces. These in turn were used to project the outcome from various permutations and combinations of completion parameters and well spacing in a multi-stacked configuration and fed into economic analyses. Through this study, we have identified that the resultant impact of key completion drivers dynamically change over production periods as well as well spacing. Thus, some completion parameters have a high impact at only early times, while other completion parameters dominate for later production. Furthermore, when well spacing and targeting are changed, the key completion derivers may also change. This demonstrates to us that completion design, targeting, well control and well spacing should be optimized based on the field-wide development plan, honoring interaction of wells during stimulation as well as draining of the SRV's, with appropriate representation of the geomechanical behavior of the reservoir. Our integrated workflow demonstrated that data analytics expedites the process of calibration and improves predictions.

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