The Permian Basin is currently the most active unconventional resource play in North America. The combination of high quality Petrophysical and Geomechanical characteristics together with advances in horizontal drilling and completion innovations in hydraulic fracturing has allowed the successful development of several stacked reservoir targets within this basin. This paper presents an integrated multidomain workflow to better understand reservoir development. A key technical aspect of the workflow was the construction of a multi-domain machine learning based facies model. Geological and petrophysical facies were initially defined using several dozens of NMR, Sonic and Minerology logs from training wells with core-calibrated petrophysical and geomechanical models. Rock mechanical facies were defined from sonic data and geological data integrated with the ISIP closure gradients and pressure matching for hundreds of stages. Finally, flow facies were defined based on the guidance of the geological facies and integration of core data .A 3D geo-model was constructed, and sector models were cut across multi areas of interest where high tier engineering datasets (micro-seismic, dfits, core, high-tier logs, tracers, PVT, BHP, etc.) existed. Fracture modeling and production modeling was performed in multiple areas in parallel to validate the properties. Using the presented workflow, different landing targets, spacing configurations, parent-child relationships and operational timing/ sequencing were evaluated to understand the optimal development strategy