Abstract
AnnotationGrouping of many features of a well stock of layer of the large-scale deposit of oil of Western Siberia with use of artificial neural network was carried out. For grouping the initial set including 555 objects was used, 95 % were chosen from them as the training set and 5 % as test. For training of neural network 17 features characterizing both geological and physical, and technological parameters of layer were accepted. Based on the results of tuning and subsequent training of the neural network, four groups of wells were identified, the closest in their geological and technological parameters. For each group of wells, in operation, parameters characterizing the uniqueness of the selected group were described. The binding is given to localization in the spatial relation of layer and to remaining reserves of oil. For each group recommendations about involvement of remaining reserves of oil in active development were offered.
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