Geomechanical Property Evaluation of the Pennsylvanian Strawn-Canyon Group Using Machine-Learning Methods: Insights from the Salt Creek Field, Midland Basin, Kent County, Texas

Geomechanical properties of rock are critical in determining fracture shapes and designing hydraulic fracturing procedures. These properties are usually obtained from static laboratory measurements but are limited to borehole locations. However, in the absence of static properties, dynamically derived properties from surface seismic data can be used. By integrating 3D poststack seismic and wireline-log data, and using machine-learning procedures, we generated volumes of three dynamic properties: namely, Young’s modulus (E), Mu-Rho (MR), and brittleness (BRI) to characterize the Salt Creek carbonate reservoir located in the Midland Basin, Kent County, Texas. By generating crossplots, we show that (1) E versus MR, and E versus BRI exhibit strong linear relationship with R2 of 0.96, and 0.71 respectively. (2) On time slices, porous zones can easily be identified, characterized by lower Gpa values compared to the surrounding non-porous rocks. (3) Because each of these properties can be correlated with photoelectric-factor (PEF) logs, and because PEF log values can indicate calcite richness and porosity within a mixed carbonate-siliciclastic system, any of the three geomechanical properties, particularly E and MR, can be used to identify rigid rock layers within the reservoir and to deduce porous zones and possible lithologies.