Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes

A technique is proposed that calculates derivative and volatility attributes from just a few well log curves to assist in brittleness index predictions from sparse well-log datasets with machine learning methods. Six well-log attributes are calculated for selected recorded well logs: the first derivative, the moving average of the first derivative, the second derivative, the logarithm of the instantaneous volatility, the standard deviation of volatility, and the moving average of volatility. These attributes make it possible to extrapolate brittleness index calibrations from the few cored and comprehensively logged wells to surrounding wells in which only minimal well-log suites are recorded. Data from two cored wells penetrating the lower Barnett Shale with distinct lithology and five well logs recorded are used to demonstrate the technique. Based on multi-K-fold cross validation analysis, the data matching K-nearest neighbour machine learning model provides the most accurate brittleness index predictions, closely followed by tree-ensemble models. For this dataset, recorded data from three well logs plus calculated attributes matches the brittleness index prediction accuracy that is achieved by the five recorded logs. Moreover, any one of the logs plus their calculated attributes yields better brittleness index prediction performance than that achieved by a combination of just those three recorded well logs. Analysis of the Gini indices of the tree-ensemble models reveals the relative influences of the recorded logs and their attributes on the brittleness index prediction solutions. Such information is used to perform feature selection to optimize the well-log attributes involved to generate reliable brittleness index predictions.Cited as: Wood, D. A. Predicting brittleness indices of prospective shale formations from sparse well-log suites assisted by derivative and volatility attributes. Advances in Geo-Energy Research, 2022, 6(4): 334-346. https://doi.org/10.46690/ager.2022.04.0