Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks
Abstract:Shear sonic logs are critical for formation evaluation, rock physics, quantitative reservoir characterization, and geomechanical studies. Although empirical and conventional machine learning (ML) have been used for shear sonic slowness estimates, both approaches suffer from multiple fundamental problems. New approaches to ML, namely, unsupervised multivariate time series clustering (Toeplitz inverse covariance-based clustering) and class-based ensemble ML, are integrated with geologic information and petrophys… Show more
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