Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using general geostatistical methods coupled with data-driven modeling. The results showed that synthetic geomechanical well logs and real field logs fall into each other when the input dataset has not seen the real field well logs. Geomechanical distributions of the Marcellus shale improved significantly when full-field data is incorporated in the geostatistical calculations. 1. Introduction Shale gas reservoirs, which are also called source rock reservoirs (SRR), have some unique attributes that make hydraulic fracturing an essential option in order to commence an economic level of the natural gas production. Unlike conventional gas reservoirs, insufficient permeability, ultra-low porosity of shale rock, and limited reservoir contact area, but vastly organic-rich formation, cannot offer production in a commercial value without stimulation processes. Many studies are conducted from shale pore-scale level to field scale reservoir simulations to improve the understanding of complex flow behavior that are developed and discussed through numerical, analytical, and semianalytical reservoir models for unconventional reservoirs [1–10]. However, in order to predict the performance of a shale gas reservoir, implementing accurate shale rock properties is essential for developing a geologic model for the entire asset. Hence, it is very critical to access more data while working on an unconventional reservoir. In this study, synthetic data are generated using artificial intelligence and data mining techniques (AI&DM). Principal stress profile of an oil and gas reservoir depends highly on the rock
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