A comparison of student attitudes toward guidance services and faculty perception of student attitudes toward these services in a three year diploma school of nursing

Thesis (Ed.M.)--Boston Universit

Geostatistical Earth modeling of cyclic depositional facies and diagenesis

In siliciclastic and carbonate reservoirs, depositional facies are often described as being organized in cyclic successions that are overprinted by diagenesis. Most reservoir modeling workflows are not able to reproduce stochastically such patterns. Herein, a novel geostatistical method is developed to model depositional facies architectures that are rhythmic and cyclic, together with superimposed diagenetic facies. The method uses truncated Pluri-Gaussian random functions constrained by transiograms. Cyclicity is defined as an asymmetric ordering between facies, and its direction is given by a three-dimensional vector, called shift. This method is illustrated on two case studies. Outcrop data of the Triassic Latemar carbonate platform, northern Italy, are used to model shallowing-upward facies cycles in the vertical direction. A satellite image of the modern Bermuda platform interior is used to model facies cycles in the windward-to-leeward lateral direction. As depositional facies architectures are modeled using two Gaussian random functions, a third Gaussian random function is added to model diagenesis. Thereby, depositional and diagenetic facies can exhibit spatial asymmetric relationships. The method is applied in the Latemar carbonate platform that experiences syn-depositional dolomite formation. The method can also incorporate proportion curves to model non-stationary facies proportions. This is illustrated in Cretaceous shallow-marine sandstones and mudstones, Book Cliffs, Utah, for which cyclic facies and diagenetic patterns are constrained by embedded transition probabilities

Communautés isolées : survie individuelle et collective en temps d’Apocalypse dans les romans de Christian Guay-Poliquin

Cette thèse de maîtrise propose une étude de la représentation des communautés géographiquement éloignées en temps d’Apocalypse et de la survie des individus qui en font partie à partir de la trilogie romanesque de Christian Guay-Poliquin. Cette trilogie comprend Le fil des kilomètres (2013), Le poids de la neige (2016) et Les ombres filantes (2021). Dans le premier chapitre, j’analyse le concept de l’eschatologie et de l’imaginaire de la fin principalement à partir des théories de Bertrand Gervais et Jean-Paul Engélibert. Je déconstruis l’imaginaire de la fin en étapes, et j’explique pourquoi les œuvres de Guay-Poliquin font partie de ce courant littéraire. Je fais un survol de l’effondrement de la société dans Le fil des kilomètres et Le poids de la neige. De plus, j’explique comment l’intertextualité contribue à cet imaginaire. Dans le deuxième chapitre, j’analyse comment la théorie de motivation humaine du psychologue Abraham Maslow peut servir de modèle pour comprendre comment les communautés se reconstruisent dans le corpus à l’étude. J’étudie ensuite comment les personnages de Guay-Poliquin dans Le poids de la neige et Les ombres filantes tentent de réinstaurer les structures sociales du passé. Dans certains cas, les modèles de restructuration font allusion à la « mentalité de garnison » développé par Northrop Frye, mais je démontre que ce type de structure sociale ne résiste généralement pas à l’épreuve de la durée. Dans le troisième chapitre, je situe la trilogie romanesque non seulement parmi les œuvres de l’imaginaire de la fin, mais parmi les œuvres de la néorégionalité. La distance qui sépare les personnages de Guay-Poliquin de la civilisation crée un environnement viril, qui devient problématique pour les femmes et les enfants. Dans ce chapitre, je propose que les femmes jouent un rôle essentiel dans la réinvention des structures sociales. Pour habiter cette ère d’incertitude, les femmes et les enfants semblent suggérer que les structures sociales doivent être fondées sur une éthique du care qui permet de répondre aux besoin plus avancés des individus et des communautés de cet univers. Cette thèse fait l’étude de la survie sur les plans individuel et collectif au sein de communautés situées en région et dans un contexte apocalyptique. Les personnages de Guay-Poliquin sont placés dans un univers qui ne leur permet pas de franchir la frontière de la fin, car celle-ci est devenue une ère, dans laquelle le passé, tout comme l’expérience du présent et l’anticipation de l’avenir, doit être réévalué

Topological characteristics of oil and gas reservoirs and their applications

We demonstrate applications of topological characteristics of oil and gas reservoirs considered as three-dimensional bodies to geological modeling.Comment: 12 page

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

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

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

Geostatistical modeling and spatial distribution analysis of porosity and permeability in the Shurijeh-B reservoir of Khangiran gas field in Iran

The main objectives of this study are analysis of spatial behavior of the porosity and permeability, presenting direction of anisotropy for each variable and describing variation of these parameters in Shurijeh B gas reservoir in Khangiran gas field. Porosity well log data of 32 wells are available for performing this geostatistical analysis. A univariate statistical analysis is done on both porosity and permeability to provide a framework for geostatistical analysis and modeling. For spatial analysis of these parameters, the experimental semivariogram of each variable in different direction as well as their variogram map plotted to find out the direction of anisotropy and their geostatistical parameters such as range, sill, and nugget effect for later geostatistical work and finally for geostatistical modeling, two approaches kriging and Sequential Gaussian Simulation are used to get porosity and permeability maps through the entire reservoir. All of statistical and geostatistical analysis has been done using GSLIB and PETREL software. Maximum and minimum direction of continuity are found to be N75W and N15E, respectively. Geostatistical parameters of calculated semivariogram in this direction like range of 7000 m and nugget of 0.2 are used for modeling. Both kriging and SGS method used for modeling but kriging tends to smooth out estimates but on the other hand SGS method tends to show up details. Cross-validation also used to validate the generated modeling