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There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development. [ABSTRACT FROM AUTHOR]

• Groundwater level prediction for a long period of time is considered in this paper. • We proposed a graph-based machine learning using which the spatial relationships are taken into account. • Our results indicate that spatial correlations are helpful when one deals with geospatial data. • The proposed method is compared with standard techniques. Accurate prediction of groundwater level (GWL) over a period of time is of great importance for groundwater resources management. Machine learning techniques due to their great performance have recently been used for this problem. Previous methods, however, did not consider the spatial relationships between wells due to the difficulty to handle unstructured well location data. In this paper, a graph neural network (GNN) is used to forecast groundwater dynamics where it can represent each well as a node in the graph. The spatial information is, thus, extracted from an interconnected network using graph convolution layers with a self-adaptive adjacency matrix. The temporal features of the sequence are obtained by gated temporal convolutional networks. The model was applied and evaluated for wells in the southwest area of British Colombia in Canada using data about 11 years (2010–2020). The proposed model performs better in terms of all the defined evaluation metrics, when compared with two baseline models: long short-term memory (LSTM) and gated recurrent units (GRU). Moreover, when the spatial dependencies are completely unknown, the model can still learn them from the data and obtain comparable performance. Furthermore, the proposed model has a high efficiency since it can simultaneously model GWL change for all monitoring wells in the system. We also demonstrated that the spatial dependencies between each well could be intuitively interpreted from the learned adjacency matrix. [ABSTRACT FROM AUTHOR]

• A convolutional neural network-based method to calculate the composite system reliability indices. • A data generation scheme to both train the CNN and calculate reliability indices. • Training and testing algorithm for the CNN to calculate composite system reliability indices. • Developing mathematical model to calculate composite system reliability indices. This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development. [ABSTRACT FROM AUTHOR]