Abstract: The invention is a method of determining an electrofacies interpretation of measurements relative to at least a portion of at least one well drilled through an underground formation. The method comprises applying supervised or unsupervised classification methods to measurements in order to determine learning information. Supervised classification methods are subsequently applied to the measurements, the classification methods being trained by learning information. An ensemble classification method is then applied to the results of the supervised classification methods to determine the electrofacies interpretation of the measurements.

Abstract: The present invention is a method of detecting and counting a geological constituent (cge) of an acquired image of a rock sample (IER), by a location detection machine learning method (ALG).

Abstract: The present invention relates to a method of detecting a geological constituent (cge) of a rock sample, by use of a machine learning method (ALG) trained with a limited number of training images (IAP), through segmentation (SEG) and partitioning (DEC) of training images (IAP) into multiple patches.

Abstract: The present invention relates to a rock classification method wherein at least one image (IMA) of the rock to be classified is acquired, and wherein a decision tree (ARB) classifying the rocks according to several descriptors is used, as well as a machine learning method (APP) from a rock image database (BIR). Machine learning is applied for each descriptor considered.