Modern geological practices, in both industry and academia, rely largely on a legacy of observational data at a range of scales. However, widespread ambiguities in the petrographic description of rock facies reduce the reliability of descriptive data. Previous studies have demonstrated a great potential for the use of convolutional neural networks (CNNs) in the classification of facies from digital images; however, it remains to be determined which of the available CNN architectures performs best for a geological classification task. We evaluate the ability of top-performing CNNs to classify carbonate core images using transfer learning, systematically developing a performance comparison between these architectures on a complex geological dataset. Three datasets with orders of magnitude difference in data quantity (7000–104,000 samples) were created that contain images across seven classes from the modified Dunham Classification for carbonate rocks. Following training of nine different CNNs of four architectures on these datasets, we find the Inception-v3 architecture to be most suited to this classification task, achieving 92% accuracy when trained on the larger dataset. Furthermore, we show that even when using transfer learning the size of the dataset plays a key role in the performance of the models, with those trained on the smaller datasets showing a strong tendency to overfit. This has direct implications for the application of deep learning in geosciences as many papers currently published use very small datasets of less than 5000 samples. Application of the framework developed in this research could aid the future of deep learning based carbonate classification, with further potential to be easily modified to suit the classification of cores originating from different formations and lithologies. 

Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification

Global trends toward physical inactivity and the replacement of healthy dietary behaviors with unhealthy food consumption, particularly in late adolescence, were commonly observed. Thus, the aim of this work was twofold: (1) to examine congruence between PAPs and DPs by identifying sex differences in healthy and unhealthy behaviors and (2) to assess the explanation behind why certain participants were classified into clusters using BMI and fat mass covariates. Late adolescents (19–21 years of age, n = 107) were selected to study. PAPs and DPs were assessed with questionnaires (IPAQ, QEB) and body height, weight, and fat mass percentage were self-reported (the accuracy and reliability of self-reported data were empirically verified). A cophylogenetic approach with several clustering procedures (heatmaps and tanglegrams) was the way to solve the stated problems. Results showed that students achieved the minimal level of physical activity, lower than students from other regions in Poland, Czech Republic, or Germany. There was congruence between PAPs and DPs in some males’ and females’ behaviors. Significant similarities in sex patterns of behaviors were revealed (Mantel tests–PAPs: r = 0.70, p < 0.001; DPs: r = 0.84, p < 0.001). Similarities in PAPs were related to transportation cycling and school/work activities behaviors. Non-healthy triads in dietary behaviors were found: fast-food, sweetened beverages, and alcoholic drinks in the first, and sweets, fried meals, and yellow cheese in the second. One healthy diad was revealed: vegetables and fruits. Only females’ dietary behaviors were reflected in body mass index (F = 3.19, p = 0.049), percentage of body fat (F = 3.87, p = 0.027), and fat mass index (F = 3.83, p = 0.028). The usefulness of the cophenetic approach in patterns study was verified. Sex similarities and specific disparities could help preparing targeted tailored intervention programs that improve healthy habits in late adolescents. This is especially important in relation to females, who more strongly reflected dietary behaviors in body composition.

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Congruence between Physical Activity Patterns and Dietary Patterns Inferred from Analysis of Sex Differences in Lifestyle Behaviors of Late Adolescents from Poland: Cophylogenetic Approach

Abstract Estimation of porosity at a millimeter scale would be an order of magnitude finer resolution than traditional logging techniques. This enables proper description of reservoirs with thin layers and fine scale heterogeneities. To achieve this, we propose an end-to-end convolutional neural network (CNN) regression model that automatically predicts continuous porosity at a millimeter scale resolution using two-dimensional whole core CT scan images. More specifically, a CNN regression model is trained to learn from routine core analysis (RCA) porosity measurements. To characterize the performance of such approach, we compare the performance of this model with two linear regression models trained to learn the relationship between the average attenuation and standard deviation of the same two-dimensional images and RCA porosity. Our investigations reveal that the linear models are outperformed by the CNN, indicating the capability of the CNN model in extracting textures that are important for porosity estimations. We compare the predicted porosity results against the total porosity logs calculated from the density log. The obtained results show that the predicted porosity values using the proposed CNN method are well correlated with the core plug measurements and the porosity log. More importantly, the proposed approach can provide accurate millimeter scale porosity estimations, while the total porosity log is averaged over an interval and thus do not show such fine scale variations. Thus, the proposed method can be employed to calibrate the porosity logs, thereby reducing the uncertainties associated with indirect calculations of the porosity from such logs. 

/pdf/automated-porosity-estimation-using-ct-scans-of-extracted-250dr8yl.pdf

Automated porosity estimation using CT-scans of extracted core data

X-ray computerized tomography (CT) images as digital representations of whole cores can provide valuable information on the composition and internal structure of cores extracted from wells. Incorporation of millimeter-scale core CT data into lithology classification workflows can result in high-resolution lithology description. In this study, we use 2D core CT scan image slices to train a convolutional neural network (CNN) whose purpose is to automatically predict the lithology of a well on the Norwegian continental shelf. The images are preprocessed prior to training, i.e., undesired artefacts are automatically flagged and removed from further analysis. The training data include expert-derived lithofacies classes obtained by manual core description. The trained classifier is used to predict lithofacies on a set of test images that are unseen by the classifier. The prediction results reveal that distinct classes are predicted with high recall (up to 92%). However, there are misclassification rates associated with similarities in gray-scale values and transport properties. To postprocess the acquired results, we identified and merged similar lithofacies classes through ad hoc analysis considering the degree of confusion from the prediction confusion matrix and aided by porosity–permeability cross-plot relationships. Based on this analysis, the lithofacies classes are merged into four rock classes. Another CNN classifier trained on the resulting rock classes generalize well, with higher pixel-wise precision when detecting thin layers and bed boundaries compared to the manual core description. Thus, the classifier provides additional and complementing information to the already existing rock type description.

/pdf/lithology-classification-of-whole-core-ct-scans-using-2crblv6p9s.pdf

Lithology classification of whole core CT scans using convolutional neural networks

Lithology is one of the critical parameters influencing drilling operations and reservoir production behavior. Well completion is another important area where facies type has a crucial influence on fracture propagation. Geological formations are highly heterogeneous systems that require extensive evaluation with sophisticated approaches. Classification of facies is a critical approach to characterizing different depositional systems. Image classification is implemented as a quick and easy method to detect different facies groups. Artificial intelligence (AI) algorithms are efficiently used to categorize geological formations in a large dataset. This study involves the classification of different facies with various supervised and unsupervised learning algorithms. The dataset for training and testing was retrieved from a digital rock database published in the data brief. The study showed that supervised algorithms provided more accurate results than unsupervised algorithms. In this study, the extreme gradient boosted tree regressor was found to be the best algorithm for facies classification for the synthetic digital rocks.

/pdf/classifying-facies-in-3d-digital-rock-images-using-225r3phx.pdf

Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches

\n X-ray computerized tomography (CT) is a nondestructive method of providing information about the internal composition and structure of whole core reservoir samples. In this study we propose a method to classify lithology. The novelty of this method is that it uses statistical and textural information extracted from whole core CT images in a supervised learning environment. In the proposed approaches, first-order statistical features and textural grey-level co-occurrence matrix (GLCM) features are extracted from whole core CT images. Here, two workflows are considered. In the first workflow, the extracted features are used to train a support vector machine (SVM) to classify lithofacies. In the second workflow, a principal component analysis (PCA) step is added before training with two purposes: first, to eliminate collinearity among the features and second, to investigate the amount of information needed to differentiate the analyzed images. Before extracting the statistical features, the images are preprocessed and decomposed using Haar mother wavelet decomposition schemes to enhance the texture and to acquire a set of detail images that are then used to compute the statistical features. The training data set includes lithological information obtained from core description. The approach is validated using the trained SVM and hybrid (PCA + SVM) classifiers to predict lithofacies in a set of unseen data. The obtained results show that the SVM classifier can predict some of the lithofacies with high accuracy (up to 91% recall), but it misclassifies, to some extent, similar lithofacies with similar grain size, texture, and transport properties. The SVM classifier captures the heterogeneity in the whole core CT images more accurately compared with the core description, indicating that the CT images provide additional high-resolution information not observed by manual core description. Further, the obtained prediction results add information on the similarity of the lithofacies classes. The prediction results using the hybrid classifier are worse than the SVM classifier, indicating that low-power components may contain information that is required to differentiate among various lithofacies.

Classifying Lithofacies from Textural Features in Whole Core CT-Scan Images

Abstract Motivation A tanglegram is a plot of two-tree-like diagrams, one facing the other, and having their labels connected by inter-tree edges. These two trees, which could be both phylogenetic trees and dendrograms stemming from hierarchical clusterings, have thus identically labelled leaves but different topologies. As a result, the inter-tree edges of a tanglegram can be intricately tangled and difficult to be analysed and explained by human readers. To better visualize the tanglegram (and thus compare the two dendrograms) one may try to untangle it, i.e. search for that series of flippings of the various branches of the two trees that minimizes the number of crossings among the inter-tree edges. The untanglement problem has received significant interest in the past decade, and several techniques have been proposed to address it. These techniques are computationally efficient but tend to fail at finding the global optimum configuration generating the least tangly tanglegram. Results We leverage the existing results to propose untanglement methods that are characterized by an overall slower convergence method than the ones in the literature, but that produce tanglegrams with lower entanglements. Availability and implementation One of the algorithms is implemented in Python, and available from https://github.com/schlegelp/tanglegram.

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Kurdistan Chawshin

Papers

Classifying Lithofacies from Textural Features in Whole Core CT-Scan Images

Automated porosity estimation using CT-scans of extracted core data

Lithology classification of whole core CT scans using convolutional neural networks

Shuffle & untangle: novel untangle methods for solving the tanglegram layout problem