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Experimental Investigation of Molecular Hydrogen Effect on Cement Related to Well Integrity for Underground Hydrogen Storage
No full textUnderground Hydrogen Storage (UHS) in the subsurface is an alternative to overcome limitations associated with a fluctuating production of renewable energy sources. The excess energy produced can be converted to hydrogen and stored in porous media. Hydrogen is injected and produced from geological formations via wells. There are several concerns regarding the interactions of hydrogen with the different well components responsible for maintaining proper well integrity, such as cement. This experimental study investigates the interaction between molecular hydrogen and cement Class H. The methodology provides a comparison of experimental tests to highlight the effects hydrogen can have on cement during aging. Samples were cured in two different environments were then characterized using different physical and chemical tests such as Unconfined Compressive Strength (UCS), Fourier Transform Infrared Spectroscopy (FTIR), Porosity and Permeability (PP), Nuclear Magnetic Resonance (NMR), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDS). Samples exposed to molecular hydrogen exhibited a greater compressive strength in both the short and long terms. Porosity was found to be lower in samples exposed to hydrogen. Similarly, permeability was lower in samples exposed to hydrogen. NMR distributions showed a reduction of larger pores and an increase of smaller pore spaces. The compositional analysis demonstrated that hydrogen presence increases the amount of hydrated product (portlandite and ettringite) formed. The crystalline needle-shaped structure observed in the SEM confirmed the presence of portlandite and ettringite within some of the larger pores. Acoustic velocities showed hydrogen- exposed samples to be stiffer and less flexible. This could be detrimental because, during UHS, cement is exposed to multiple cycles of injection and production. This experimental study showed that molecular hydrogen present could generate the formation of hydrated products, causing a shift in the porosity distribution affecting the mechanical strength of the cement
Selected examples of the Crusader style icons in the Fred Jones Jr. Museum of Art
Get PDFBefore Constantinople fell in 1453, Byzantine-style iconography had been experiencing massive shifts in not only production, but also in manufacturing quantity and personal use. Two such pieces reminiscent of this shift are displayed in the Fred Jones Jr. Museum of Art as part of the Ambassador George Crews and Cecilia DeGoyler McGhee Collection of Icons at the Fred Jones Jr. Museum of art at The University of Oklahoma at Norman. These two works, Virgin and Child, Virgin of Consolation (Madonna della Consolazione) and The Virgin as the Life-Giving Spring are representative of the aforementioned variables, namely the effects of Crusaders and their art on the iconological canon, the emergence and prevalence of icons hailing from the Greek Ionian islands, and the influence of private patronage on thematic matter and function. By creating a historiographical timeline focusing on the influence of these variables of style, I identify the icons in the FJJMC as derivates of the works being manufactured around the thirteenth to fifteenth centurie
New mothers' management of complaints in computer-mediated communication channels
Get PDFThis study examines new mothers’ complaining behaviors in computer-mediated communication (CMC) channels and investigates mothers’ privacy management when self-disclose family issues online. This study includes data collected through face-to-face in-depth interviews and interview surveys with new mothers (i.e., who had at least one child younger than 3 years old at the time), which were analyzed through constant comparative methods and descriptive statistics. In total, 35 participants were recruited for this study, including 16 American mothers and 19 Chinese mothers who live in the US. The study examines new mothers’ complaining behaviors in CMC channels through two steps. First, I explore the facilitators and barriers that influence new mothers’ choices when selecting the appropriate CMC channel(s) to complain about motherhood-related challenges and problems. Then I categorize these facilitators and barriers into four areas of consideration (i.e., emotion management, impression management, information control, and problem-solving) that mothers may think of when balancing the benefits and risks of using any CMC channel to express their negative feelings. Based on the Communication Privacy Management theory, I also investigate mothers’ self-disclosure behaviors in different channels. From level 1 (vague) to level 5 (full of details), mothers disclosed sensitive and private family issues with varying levels when complaining in different channels. The four areas of consideration can be applied again to explain their choices. I further identify four underlying factors across these areas of consideration that affect new mothers’ online complaining behavior: mothers’ expected social support types, the nature of the complaining subject, online privacy literacy, and cultural differences. Finally, I propose an integrated model of negative self-disclosure via CMC which demonstrates all the factors that potentially impact people’s channel selection and message-framing processes
Mythologies of the migrant caravans: religion, ideology and migration
Get PDFThis research study is a semiotic and hermeneutic analysis of the tweets published on Twitter about the migratory caravan that occurred in 2018 under the hashtags #viacrucismigrante, #caravanamigrante #caravanscoming, and #migrantcaravan, in Spanish and English from March 25th to April 28th, 2018. The texts of the tweets studied included the caravan's symbols, mythologies, representations, and messages favoring or opposing the migrant caravan. This study’s findings underscore mythological imaginaries about migrant caravans, migration, borders, and nations, elements sometimes linked to ideas of the past and the future. Also, the 2018 migrant caravan incorporated characteristics of a social movement as a strategy to confront the limitations of migration in the region. The migrant caravan used the narrative of the way of the cross to reinforce group identity, and this narrative was contested with the imaginary of an invasion. In the analysis, five themes were identified: a) caravan journey; b) forced to leave; c) religious; d) demands; e) threats. The messages about or against the caravan include imaginaries about the past and the future. Past times are viewed as a romanticized era presented as a paradigm of the future, a time living in the collective memory and needing to be rescued for the greatness of a nation. The future is represented as hope and tied to religious penitence and travel through which redemption will be achieved in the form of a new life. These two myths are expressed in connotations, narratives, religious metaphors, and intertextualities in the texts published on Twitter regarding the 2018 way of the cross migrant caravan during the timeframe studied. Aspects of solidarity were more frequent in the Spanish tweets than in English ones, where the threat theme was more frequent. The context of the caravan was characterized by polarized debate about migration during Donald Trump’s administration as president of the United States
Aerial photo index, Garvin County, OK. Section 3 of 5
No full textAerial photograph index (photomosaic), Garvin County, Oklahoma. MAP 3 of
Aerial photo index, Muskogee County, OK. Section 8 of 15
No full textAerial photograph index (photomosaic), Muskogee Co. Okla. -AU- MAP 8 of 1
Aerial photo index, Hughes County, OK. Section 8 of 9
No full textAerial photograph index (photomosaic), Hughes Co. OKLA. -AWH- MAP 8 of
Enhanced Capsule-based Networks and Their Applications
Get PDFCurrent deep models have achieved human-like accuracy in many computer vision tasks, even defeating humans sometimes. However, these deep models still suffer from significant weaknesses. To name a few, it is hard to interpret how they reach decisions, and it is easy to attack them with tiny perturbations.
A capsule, usually implemented as a vector, represents an object or object part. Capsule networks and GLOM consist of classic and generalized capsules respectively, where the difference is whether the capsule is limited to representing a fixed thing. Both models are designed to parse their input into a part-whole hierarchy as humans do, where each capsule corresponds to an entity of the hierarchy. That is, the first layer finds the lowest-level vision patterns, and the following layers assemble the larger patterns till the entire object, e.g., from nostril to nose, face, and person.
This design enables capsule networks and GLOM the potential of solving the above problems of current deep models, by mimicking how humans overcome these problems with the part-whole hierarchy. However, their current implementations are not perfect on fulfilling their potentials and require further improvements, including intrinsic interpretability, guaranteed equivariance, robustness to adversarial attacks, a more efficient routing algorithm, compatibility with other models, etc.
In this dissertation, I first briefly introduce the motivations, essential ideas, and existing implementations of capsule networks and GLOM, then focus on addressing some limitations of these implementations. The improvements are briefly summarized as
follows. First, a fast non-iterative routing algorithm is proposed for capsule networks, which facilitates their applications in many tasks such as image classification and segmentation. Second, a new architecture, named Twin-Islands, is proposed based on GLOM, which achieves the many desired properties such as equivariance, model interpretability, and adversarial robustness. Lastly, the essential idea of capsule networks and GLOM is re-implemented in a small group ensemble block, which can also be used along with other types of neural networks, e.g., CNNs, on various tasks such as image classification, segmentation, and retrieval
The relationship between certification status and teacher evaluation: an analysis in a large urban district
No full textTeacher shortages are a growing problem across the United States. As one means of addressing this problem, policy makers have sought to loosen barriers to the profession by creating provisions for emergency certification. Emergency-certified teachers require no teaching experience, although they are required to have some experience working in the subject area. Research on emergency-certified teachers reports that these teachers have lower value-added measures, but this literature lacks information on how these teachers perform on teacher evaluations. In this study, the relationship between teacher evaluation scores and a teachers’ certification status is investigated. Data are derived from a sample of teachers (n = 1285) in a large urban district. Results indicate small but significant associations between teacher certification and evaluation scores, with a difference in scores between -0.22 and -0.80. Furthermore, the results are consistent across different statistical models
Applying machine learning models to diagnose failures in electrical submersible pumps
Get PDFElectrical Submersible Pump (ESP) failures are unanticipated but common occurrences in oil and gas wells. It is necessary to detect the onset of failures early and prevent excessive downtime. This study proposes a novel approach utilizing multi-class classification machine learning models to predict various ESP specific failure modes (SFM’s). A comprehensive dataset and various machine learning algorithms are utilized. The prediction periods of 3 hours to 7 days before the failure are evaluated to minimize false alarms and predict the true events.
The ML models are based on field data gathered from surface and downhole ESP monitoring equipment over five years of production of 10 wells. The dataset includes the failure cause, duration of downtime, the corresponding high-frequency pump data, and well production data. According to these data, most ESP operational failures are characterized as electrical failures.
Four modeling designs are used to handle the data and transform them into actionable information to predict various ESP failure modes at different prediction periods. Several ML models are tested and evaluated using precision, recall, and F1-score performance measures. The K-Nearest Neighbor (KNN) model outperforms the other algorithms in forecasting ESP failures. Some other tested models are Random Forest (RF), Decision Tree (DT), Multilayer Perceptron (MLP) Neural Network, etc. The findings of these ML models reveal that as the prediction period extends beyond three days, it becomes more challenging to predict the true failures. Furthermore, all tested designs show similarly good performances in predicting ESP specific failures. The design that integrates the impacts of gas presence and pump efficiency while minimizing the number of input variables is suggested for general use.
Based on the field data, a Weibull model is built to estimate the probability of failure. The mean time between failure (MTBF) values are utilized as inputs to the Weibull analysis. The Weibull shape and scale parameters are estimated using Median Rank Regression. Then the Weibull Probability plots are generated with high R2 values (86.5-99.4%) and a low p-value for all wells. The results show increases in pump unreliability with time for all the wells.
By integrating the outcomes of the ESP Failure prediction ML model with the Weibull unreliability model, a powerful tool is provided. This tool allows the engineers to detect failures early, diagnose potential causes, and propose preventive actions. It is crucial in aiding the operators in transitioning from reactive to proactive and predictive maintenance of artificial lift operations