Theses and DissertationsThis community contains master's Theses and Dissertations in all the courses offered in AUST, from 2009-2022.http://repository.aust.edu.ng/xmlui/handle/123456789/3482024-03-28T19:48:27Z2024-03-28T19:48:27ZReservoir Simulation Framework to Support Marginal Field Development PlanningAkpobasaha, Ogheneroborhttp://repository.aust.edu.ng/xmlui/handle/123456789/51382024-01-08T22:01:03Z2023-05-05T00:00:00ZReservoir Simulation Framework to Support Marginal Field Development Planning
Akpobasaha, Oghenerobor
Dynamic simulators are practical tools used in the oil and gas industry to help make informed decisions, optimize production, reduce risks, and maximize hydrocarbon recovery. They are fundamental for the success and profitability of oil and gas operations, playing a vital role in reservoir engineering and management practices. The objective of this study is to propose an optimal development framework for a marginal field located offshore in the southeast Niger Delta, where only available data are from neighboring fields. The process involves estimating volumes of hydrocarbon-bearing sands through reservoir characterization and static modeling, developing a simulation model, and using it, along with decline curve analysis, to estimate produced hydrocarbon volumes. Next, production constraints are formulated for infill wells using a well simulator to determine optimal flow rates and tubing sizes. An optimized production strategy is then developed by analyzing the sensitivity of the constraints and parameters to oil recovery. Sensitivity analyses were conducted on Tubing Head Pressure (THP) and injection rates to identify the most effective production strategy throughout
the 15-year life of field Finally, an economic analysis is performed to assess the project profitability. The study also includes identifying and assessing the environmental, subsurface, and surface risks associated with the field development plan. Based on these findings, it is recommended that the proposed reservoir simulation framework used in the Ratson Sand C project can be applied to similar fields to achieve maximum recovery.
Main Thesis
2023-05-05T00:00:00ZAB Initio Study of Surface Energy, Surface Stress and Coupling Coefficient of Au (111)Shehu, Mustaphahttp://repository.aust.edu.ng/xmlui/handle/123456789/51372023-12-18T22:01:50Z2023-12-10T00:00:00ZAB Initio Study of Surface Energy, Surface Stress and Coupling Coefficient of Au (111)
Shehu, Mustapha
This study investigates ab initio exploration of the Au (111) surface within the generalized gradient approximation, with a primary focus on assessing the convergence properties of this noble metal. An in-depth analysis of the material's response to various computational parameters, including cutoff energy, K-point sampling, and lattice parameter, was conducted to ensure the reliability and consistency of the findings. The theoretical determination of the lattice constant, yielding a value of 4.059 Å, not only aligns quantitatively with experimental measurements but also agrees with calculated values. A noteworthy aspect of this investigation involves reporting on the work
function's response to strain, shedding light on how this essential property evolves under external
influences. Additionally, the study evaluates the variation of energy per unit cell with varying slab
thickness, providing insights into the material's behavior across different structural configurations. The results reveal that Au (111) exhibits a surface energy of 0.5561 𝑒𝑉Å−1, surface stress of 0.18177 𝑒𝑉Å−2and a coupling coefficient of 1.145 eV. These results provides significant implications for understanding the mechanisms associated with electrochemical coupling at an atomic scale, offering crucial insights into the material's behavior across diverse atomic and electronic structures. Thus this work contribute to the understanding of Au (111) surface properties, laying a foundation for advancements in understanding electrochemical phenomena and fostering the development of tailored applications in materials science and nanotechnology.
Main Thesis
2023-12-10T00:00:00ZThe Auman Integral of Set-Valued MapsEleh, Chinedu Anthonyhttp://repository.aust.edu.ng/xmlui/handle/123456789/51362023-12-18T22:01:37Z2018-05-15T00:00:00ZThe Auman Integral of Set-Valued Maps
Eleh, Chinedu Anthony
This thesis focuses on the Aumann integral of set-valued random variables and its properties. We started o by studying the space in which this integral lies: hyperspace endowed with the Hausdor metric. We considered convergence on a hyperspace with respect to the Hausdor metric and reviewed the works of Kuratowski, Mosco in trying to abstract topologically, the Hausdor convergence; this led to a comparison between weak, Wijsmann, Kuratowski-Mosco convergences to Hausdor convergence. We proceeded to see the conditions under which a set-valued random variable is measurable, integrable and integrably bounded. Finally, we de ned the class of integrable selections of an integrable set-valued random variable and used it to de ne the Aumann integral, and went further to prove and outline su cient conditions for the Aumann integral to be convex and closed-valued respectively
Main Thesis
2018-05-15T00:00:00ZHow Machine Learning Can Evaluate the Influence of Socioeconomic and Climatic Factors in Agricultural Yield: A Case of NigeriaDappa, Tamuno-Opubo Godwinhttp://repository.aust.edu.ng/xmlui/handle/123456789/51352023-09-29T21:00:47Z2023-05-10T00:00:00ZHow Machine Learning Can Evaluate the Influence of Socioeconomic and Climatic Factors in Agricultural Yield: A Case of Nigeria
Dappa, Tamuno-Opubo Godwin
The major international agencies in charge of nutrition are becoming increasingly concerned about global agricultural production in particular. Food insecurity has emerged in some populated areas, including Africa, as a result of the increased worldwide need for food as a result of record population growth. Climate change and its variability are two additional factors that contribute to world food insecurity. Furthermore, agricultural policy officials, farmers, and decision-makers require advanced technologies in order to make timely strategies or policies that will have an effect on the quality of crop harvests. Machine learning and other new, powerful analytical techniques made possible by big data technologies have already proven useful in a number of industries, including biology, finance, and medicine. The yield of three major crops, including cocoa, sesame, and cashew, at the national level in Nigeria during the course of the years spanning 1990 to 2020 is forecasted in this study using a machine learning-based prediction method. We used climatic, agricultural yield, and socioeconomic data to help policymakers and farmers anticipate the yearly agricultural outputin Nigeria. We employed k-nearest neighbors, a decision tree, and random forest. We also employed a hyper-parameter tweaking technique through cross-validation to enhance the model and avoid overfitting. For sesame, the accuracy of the Decision Tree model was the
highest, having a test accuracy of 97.92% for socioeconomic and climatic factors combined, while the KNN model did the best with a test accuracy of 99.71% for climatic components separately. The accuracy of the Random Forest model was 87.54% for climatic elements alone and 87.64% for socioeconomic and economic factors together. For cocoa, the Decision Tree model had an accuracy of 89.49% for socioeconomic and climatic factors combined and 89.51% for climatic components alone, while the KNN model had the best accuracy of 90.71% for climatic elements alone. For socioeconomic and climatic factors taken together, the Random Forest model's accuracy was 87.82%; for climatic components alone, it was 88.83%. For cashew nuts, the accuracy of the KNN model was 78.38% for socioeconomic and climatic components combined and 99.81% for climatic factors alone, compared to 88.27% for socioeconomic and climatic elements combined and 86.58% for climatic factors alone for the Decision Tree model. For both socioeconomic and climatic components combined, the Random Forest model's accuracy was 98.50%, while for climatic factors alone, it was 98.75%. In conclusion, the Random Forest model outperformed the KNN and Decision Tree models across all crop and factor combinations. Our findings indicate that machine learning algorithms can be used to forecast crop yields with reasonable accuracy when socioeconomic and meteorological variables are combined
Main Thesis
2023-05-10T00:00:00Z