The prediction of the spatial distribution of geological facies based on well log measurements and geophysical data is generally treated as a stochastic sampling or optimization problem. Approaches based on Hidden Markov Models provide satisfactory results in terms of data mismatch but the geological realism of the predicted facies model depends on the prior assumptions related to facies proportions and sequence patterns. We propose here an innovative approach based on Recursive Neural Network and we compare the results to the convolutional Hidden Markov Model approach based on first-order and higher-order Markov chains. An example of application is presented with a quantification of the accuracy of the modeling results and fitness of the probability estimates.


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