The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.
. Developing of the exploration criteria for oil reservoirs and non-structural traps in the clinoform successions are the key target as for detailed field appraisal in the West-Siberia basin well as for exploration in the clinoform complexes in the new sedimentary basins without drilling. Based on complex analysis of the seismic, well logs, well tests and core data in the northern Priobskoye field, the correlation between clinoform geometry, edge trajectory and distribution pattern of the reservoirs in the productive formation AS is established. The highest flow rates and reservoir properties are typical for bar deposits, proximal fan and slope channels. Deposits of bars are formed at the high stand system tract and at the beginning of the falling system tract on the edges of tangential clinoforms with gently ascending, flat and descending edge trajectory. Deposits of basin floor fans and slope channels are confined to the drop in the relative sea level and its low stand; it is advisable to search for them at the bottomset of tangential clinoforms with a descending edge trajectory and in gently cross-bedded clinoforms.
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