Predicting Mineralogical Composition in Unconventional Formations Using Machine Learning and Well Logging Data

Abstract Quantitative determination of mineralogy can be done using high-definition spectroscopic logging methods, however these methods are rarely used due to complexity and cost. Also, it is difficult to obtain mineralogical composition in unconventional formations due to presence of kerogen and high heterogeneity and anisotropy of such formations. This problem can be resolved by utilizing Machine Learning algorithms based on well logging and thermal profiling data which can improve and speed up reservoir characterisation. Special wrappers such as Multioutput Regressor and Regressor Chain were applied to test several machine learning models and strategies of well logs combinations on multiscale data from an unconventional formation in West Siberia to predict mass and volumetric fractions of minerals obtained from Litho Scanner. RMSE and MAE were used as regression metrics. To validate the results, theoretical model was used to calculate thermal conductivity based on mineral volume fractions and compared with experimental data. Regressor Chain showed better performance for weight fractions prediction when data was scarce. The Gradient Boosting Regressor encapsulated within a Regressor Chain exhibited the most favorable outcomes in relation to the precision of mapping. The evaluation contrasting the ML-based model with the LithoScanner exhibited an average discrepancy of 0.026, as measured by the RMSE metric.