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Item type:Item, Access status: Open Access , Item type:Item, Access status: Open Access , Real Time Identification of Geological Factuals by Integrating Formation Micro Resistivity Imaging Logs With Computer Vision.(2024-05-07) Nashed, Samuel$$\[x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}\]$$ \[\sum_{i=1}^{n} i^2 = \frac{n(n+1)(2n+1)}{6}\] \[\int_{a}^{b} f(x) \,dx = F(b) - F(a)\] \[\lim_{x \to 0} \frac{\sin x}{x} = 1\] $a^x$ Reservoir characterization is pivotal for the success of oil and gas exploration, where sedimentary features significantly influence petrophysical properties and fluid flow behavior. This paper emphasizes the importance of accurately identifying and delineating these features, including bedding, cross-bedding, faulting, and fractures, to enhance reservoir characterizatrion and efficiency. Using Formation Micro Resistivity Imaging (FMI) log as a powerful tool, this research focuses on leveraging Computer Vision (CV) and Deep Learning (DL) methodologies for the automatic analysis of FMI log. The interpretation of FMI logs holds importance in reservoir characterization, modeling, natural fracture analysis, well completion design, and stress direction identification. Real-time interpretation is crucial for geo-steering and wellbore stability assessment. This study aims to leverage the power of CV and DL to provide accurate and real-time FMI interpretations, supporting daily drilling and completion operations The extensive datasets from FMI logs were processed, segmented, and clustered to identify various geological features. The CV model was trained on diverse geologic attributes, such as partially open fractures, Planner Laminated Siltstone, Massive Pyritic Mudstone, Laminated Arg. Sandstone facies, Calcareous Fossiliferous Mudstone facies, Low Angle Cross bedding, High Angle Cross bedding, Vuggy Dolomite, Fractured Dolomite, nodular limestone, glided mudstone, and other features. Subsequently, this model was deployed in real-time operations to interpret newly recorded FMI logs, validating its accuracy alongside expert interpretations. During the model development, the optimum learning rate was found to be approximately 0.0052, successfully achieving the target. Through a carefully optimized training process, the model achieves an impressive overall accuracy of 92% in classifying over 50 geological features. Detailed insights into the model's performance are provided through the analysis of the confusion matrix and classification report. Further validating its robustness, the model is tested on a set of 100 unique images not included in the training set, showcasing a generalization capability with an accuracy exceeding 86%. The CV model was evaluated with different metrics. The accuracy, precision, recall (Sensitivity or True Positive Rate), F1 score, and AUC-ROC (Area under the Receiver Operating Characteristic curve) of the model showed exceptional reliability of the model. Exceptional reliability was demonstrated, affirming its role in oilfield digital transformation. The model facilitated prompt wellbore stability and completion decisions, enabling the timely request of special needed logs without rig nonproductive time (NPT). This research demonstrates the integration of advanced technologies in the analysis of FMI logs, offering a pathway to enhanced reservoir characterization.Item type:Item, Access status: Open Access , Revealing Insights in Evaluating Tight Carbonate Reservoirs: Significant Discoveries via Statistical Modeling. An In-Depth Analysis Using Integrated Machine Learning Strategies(2024-05-07) Nashed, SamuelMore than 65% of the world's hydrocarbon reserves are contained within carbonate reservoirs. From a geological perspective, the majority of carbonate reservoirs exhibit tight characteristics, resulting in a similar resistivity response. However, the differentiating factor lies in the presence or absence of primary and secondary porosity. Given their tight nature, these carbonate reservoirs typically exhibit lower porosity compared to conventionally produced carbonate reservoirs. The main goal of this research is to construct a workflow to evaluate Carbonate reservoir potentials, outlining methodologies for identifying facies, reservoir quality, and fractures. The characterization of heterogeneity through borehole image logs is highlighted, providing detailed information on porosity, permeability, and fracture distribution. The prediction of carbonate reservoir potentiality traditionally relies on conventional petro-physical analysis. Yet, a more sophisticated alternative emerges by leveraging machine-learning models to cluster wells according to their similarities. Then advanced approach facilitates the classification of these clusters into two categories: those that have demonstrated economic oil rates, representing successful outcomes, and those that have exhibited water or mist flow, characterizing unsuccessful cases. Clustering methods, which group elements based on similarities, offer an improved set of tests over time, enhancing the prediction of potentiality. Typically unsupervised, clustering problems lack a target for model training or direct cluster evaluation. Our developed methodology utilizes a decision tree regression for creating clusters. Tree methods effectively divide data into groups, enabling predictions based on these groups. Widely applied in petroleum-related issues, tree-based models include decision trees, assembly-based models like random forest, and gradient boost-based models such as XGBoost, LightGBM, and CatBoost. While assembly and gradient boost methods enhance prediction power. Despite decision tree methods often exhibiting inferior performance, their advantage lies in the ability to comprehend how the model divides variables and creates cuts. This transparency can offer valuable insights into the actual problem. Utilizing these cuts, we create clusters based on different conventional log features and then classify them depending on the actual results, deviating from traditional unsupervised methods that solely rely on variables. The advantage of the hybrid approach, integrating both supervised and unsupervised methods, in constructing clusters and predicting the potentiality of A5 carbonate formation, lies in the comprehensive utilization of both labeled and unlabeled data. This combined methodology harnesses the benefits of guided learning from labeled examples while also exploring patterns and structures within the data that may not be evident through explicit supervision, thereby enhancing the accuracy and robustness of potentiality predictions.Item type:Item, Access status: Open Access , Molecular dynamics simulation of DNA translocation through solid-state nanopores(2024) Watson, Micah; Tayo, Benjamin; Xu, Gang; Jiang, YuhaoThis thesis explores the use of molecular dynamics (MD) simulations to model double-stranded DNA (dsDNA) translocation through nanopores, aiming to optimize nanopore-based DNA sequencing technologies. Using both LAMMPS and NAMD, the study investigates the potential for MD simulations to provide insights into nanopore sequencing mechanisms and system behaviors. Insights gained from LAMMPS tutorials by Simon Gravelle informed the understanding of force fields, system minimization, heating, and equilibrating processes necessary for accurate MD simulations. The atom pull method presented in these tutorials was foundational in understanding force application within molecular systems, analogous to grid-steered molecular dynamics (G-SMD) techniques later applied to DNA sequencing.Following these foundational steps, NAMD simulations based on protocols by Jeffrey R. Comer were performed to simulate dsDNA translocation through two types of nanopores: an alpha-hemolysin biological nanopore and a Silicon Nitride (Si₃N₄) solid-state nanopore. The alpha-hemolysin simulation allowed for an initial examination of DNA behavior in a biological nanopore, while the Si₃N₄ nanopore simulation provided detailed ionic current signatures critical for sequencing analysis. Protocols from Comer’s nanopore modeling guide were closely followed to accurately construct these systems, and no structural modifications were made. Results indicate that nanopore geometry, such as the hourglass shape in Si₃N₄, provides a stable pathway for DNA translocation and yields consistent current signals. The simulations utilized a higher-than-normal voltage to accelerate translocation, which provided faster insights but deviated from experimental conditions, suggesting areas for methodological refinement. The high than normal voltage is due to this work following steps described 15 years ago before GPU-computing which led to simplifications in the system. These findings were compared with both another Si₃N₄ simulation and real-world experimental data using Si₃N₄. The findings suggest that MD simulations can offer valuable insights into nanopore design and sequencing efficiency, providing a foundation for further optimization of real-world DNA sequencing applications. Future work could focus on refining computational methods to simulate translocation under more realistic voltages, ultimately bridging the gap between simulation and experimental results. This study contributes to the evolving field of nanopore sequencing, suggesting avenues for improvements in nanopore structure and operational conditions to enhance sequencing accuracy and reliability.Item type:Item, Access status: Open Access , Ambivalent sexism and the impact of victim attractiveness on believability(2024) Wackler, Abigail J.; Maass, Jaclyn; Rerick, Peter; Mabry, JohnEvidence suggests that one in five college women will be sexually victimized during their time in higher education. In spite of this prevalence, it is still one of the most underreported crimes. One major factor that prevents victims from coming forward about their experiences is the fear of not being believed. Several different factors may contribute to people's likelihood of believing accounts of sexual assault. Personal perception, rape myth acceptance, and physical appearance can all contribute to believing victims. The presence of ambivalent sexism can also play a role in this. Ambivalent sexism is made up of two separate constructs: benevolence and hostility. The current research investigated the impact of victims' attractiveness on the believability of their sexual assault accounts when moderated by ambivalent sexism. Based on the halo effect, I hypothesized that a more attractive victim may be believed more than a less attractive victim (H1a). Based on victim blaming, I hypothesized that a more attractive victim may be believed less than a less attractive victim (H1b). Additionally, I hypothesized that those with high levels of hostile sexism would be more inclined to believe a more attractive victim than the less attractive victim (H2a), and those with high levels of benevolent sexism would be more likely to believe the less attractive victim compared to the more attractive victim (H2b). Participants (N = 177) read a mock sexual assault testimony paired with a confederate photograph of the supposed victim. These photographs were normed based on perceived attractiveness from the Chicago Face Database. The photographs with the highest and lowest ratings were used for the more and less attractive victim conditions, respectively. Participants then completed a believability questionnaire, the Ambivalent Sexism Inventory, and some demographic information. The entire study was completed online through Qualtrics. Hypotheses 1a and 1b were tested through an independent samples t-test; the results were not significant. Hypotheses 2a and 2b were tested using simple slope analysis. Hypothesis 2a was not supported statistically. The test for Hypothesis 2b reached marginal significance, but in the opposite direction as predicted. Those high in benevolent sexism were more likely to believe the more attractive confederate victim compared to the less attractive victim. These results could hold implications primarily for case solvability and jury selection. Limitations included a possible ceiling effect, limiting the ability to find a statistically significant effect. The population also skewed young with an average age of 25. Future research should include measures for rape myth acceptance, feminism, religiosity, and political affiliation.