Geomechanical brittleness index prediction for the Marcellus shale exploiting well-log attributes

Abstract

Geomechanical brittleness of tight formations calculated from Poisson's Ratio with Young's Modulus requires laboratory testing of cores and/or estimation from shear wave acoustic logs. In shale exploitation budgets are not available to provide those measurements in many wells and it is desirable to estimate a geomechanical brittleness index (BIg) from a basic, often limited, suite of available well logs. BIg helps field operators to select the most prospective sections of tight rocks that are likely to benefit most from fracture stimulation. A machine-learning (ML) approach that combines a few recorded well logs with derivative and volatility attributes calculated from that recorded data is applied to two wellbores that penetrate the Middle Devonian formations (Appalachian Basin). This technique is novel as it has not been previously applied to predict BIg. The results demonstrate that ML models involving just two or three recorded well logs plus selected attributes as input variables provide BIg predictions that are as accurate as ML models that involve three and five recorded well logs as input variables, respectively. Moreover, the ML models involving attributes are as generalizable as ML models based only on multiple recorded well logs when applying models trained with data from one well to predict BIg from well log data in another nearby well. Extreme gradient boosting and elastic net models provide the lowest BIg prediction errors when the trained models are applied to log data from another well. Feature importance analysis identifies that the recorded compression sonic log, bulk density and gamma ray, in that order, are the most influential features in the BIg prediction models applied to the two Marcellus Shale wells. However, two of calculated well-log attributes (moving averages of the well-log derivative and volatility) are particularly effective at improving the BIg predictions when used in combination with just those three recorded well logs. The technique is worth testing in other shale basins.