A computer-implemented method of classifying facies of a geological formation comprising: a) generating data (105, 205) characterizing properties of a portion of the geological formation as a function of depth based on well log data obtained from a plurality of different well logging tools, wherein: the data of a) is generated by preprocessing (110, 210) the well log data obtained from the plurality of well logging tools, wherein the preprocessing involves performing a dimension reduction on the well log data obtained from the plurality of well logging tools; or the data of a) is part of a layer model that includes a number of layers each associated with a vector of properties of the portion of the geological formation and layer boundaries, wherein the layer boundaries are determined by processing well log data obtained from at least one predetermined well logging tool; b) processing the data of a) to automatically classify (160, 240) or identify a set of facies for the portion of the geological formation, wherein the number of facies in the set is determined automatically (150, 230) without human input, wherein: the number of facies is determined by processing the layer-based property vectors of the layer model using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; or the number of facies is determined by processing the well log data on a depth-by-depth basis using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; and storing in electronic form data representing the set of facies determined in b) or using said data to output and display visual representation of the set of facies determined in b), for evaluating the geological formation and reservoir characterization, to improve the chances of success of exploiting the well. a) generating data (105, 205) characterizing properties of a portion of the geological formation as a function of depth based on well log data obtained from a plurality of different well logging tools, wherein: the data of a) is generated by preprocessing (110, 210) the well log data obtained from the plurality of well logging tools, wherein the preprocessing involves performing a dimension reduction on the well log data obtained from the plurality of well logging tools; or the data of a) is part of a layer model that includes a number of layers each associated with a vector of properties of the portion of the geological formation and layer boundaries, wherein the layer boundaries are determined by processing well log data obtained from at least one predetermined well logging tool; the data of a) is generated by preprocessing (110, 210) the well log data obtained from the plurality of well logging tools, wherein the preprocessing involves performing a dimension reduction on the well log data obtained from the plurality of well logging tools; or the data of a) is part of a layer model that includes a number of layers each associated with a vector of properties of the portion of the geological formation and layer boundaries, wherein the layer boundaries are determined by processing well log data obtained from at least one predetermined well logging tool; b) processing the data of a) to automatically classify (160, 240) or identify a set of facies for the portion of the geological formation, wherein the number of facies in the set is determined automatically (150, 230) without human input, wherein: the number of facies is determined by processing the layer-based property vectors of the layer model using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; or the number of facies is determined by processing the well log data on a depth-by-depth basis using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; and storing in electronic form data representing the set of facies determined in b) or using said data to output and display visual representation of the set of facies determined in b), for evaluating the geological formation and reservoir characterization, to improve the chances of success of exploiting the well. the number of facies is determined by processing the layer-based property vectors of the layer model using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; or the number of facies is determined by processing the well log data on a depth-by-depth basis using the Bayesian Information Criterion with or without combining it with the entropy method or by using a method based on the repeatability of the clustering results; and storing in electronic form data representing the set of facies determined in b) or using said data to output and display visual representation of the set of facies determined in b), for evaluating the geological formation and reservoir characterization, to improve the chances of success of exploiting the well.