Abstract
Successful recovery of hydrocarbons from the reservoirs, notably shale, is attributed to realizing the key fundamentals of reservoir rock properties. Having adequate and sufficient information regarding the variable lithology and mineralogy is crucial in order to identify the "right" pay-zone intervals for shale gas production. Also, contribution of mechanical properties (Principal stress profiles) of shale to hydraulic fracturing strategies is a well understood concept. It may also contribute to better, more accurate simulation models of production from shale gas reservoirs.
In this study, synthetic geomechanical logs (Including following properties: Poisson's Ratio, Total Minimum Horizontal Stress, Bulk and Shear Modulus, etc.) are developed for more than 50 Marcellus Shale wells. Using Artificial Intelligence and Data Mining (AI&DM), data-driven models are developed that are capable of generating synthetic geomechanical logs from conventional logs such as Gamma Ray and Density Porosity. The data-driven models are validated using wells with actual geomechanical logs that have been removed from the database to serve as blind validation wells. In addition, having access to necessary data to building a geomechanical distribution (Map and Volume) model can assist in understanding the rock mechanical behavior and consequently creating effective hydraulic fractures which is considered to be an essential step in economically development of Shale assets.
Moreover, running geomechanical logs on a subset of wells, but having the luxury of generating logs of similar quality for all the existing wells in a Shale asset can prove to be a sound reservoir management tool for better reservoir characterization, modeling and efficient production of Marcellus Shale reservoir.
