Well Logs Inversion Into Lithology Classes: Comparing Bayesian Inversion and Machine Learning

Lithology classification is a crucial challenge in geological research. Lithologies at thelocations without cores need to be predicted by using indirectly geophysics measurementssuch as well logs and seismic data. In this study, we use the spatial dependency of sedimentsand well logs data for inversion into lithologies by a kernel-based hidden Markov model(HMM) and a gated recurrent unit (GRU) model. We estimate model parameters from twotraining wells and predict lithologies from well log observations for a blind test well. Thepredictions are also compared to results from a deep neural network (DNN) model, whichassumes spatial independence. Results indicate that the lithology predictions supplied by theHMM and GRU models are more reliable than the ones from DNN in term of classificationaccuracy and make more senses in geological interpretation. Moreover, the HMM providesquantifications of the classification uncertainty.