Design and development of a hybrid near field and far field antenna measurement system, The
Advisor
Elsherbeni, Atef Z.Date issued
2021Keywords
antenna measurementsfar field measurements
near field measurements
near field to far field transformation
software interface
Metadata
Show full item recordAbstract
In this thesis, a dual-purpose antenna chamber measurement system is presented. The measurement system is an anechoic chamber where the near-fields or far-fields of an antenna can be measured with the same equipment. A custom software was developed to perform either type of measurement. This antenna chamber is a much more cost-effective solution compared to conventional antenna chambers, where separate chambers are used for different types of measurements. This thesis presents the developed software and hardware for a custom-made chamber design that produces accurate results. Both mechanical and software capabilities are introduced, but emphasis is put on the development of the software capabilities throughout this thesis. Both theoretical, numerical, and experimental analysis is introduced to verify the performance of the chamber. The necessary methods to perform the near-field to far-field transformations are presented and discussed in detail. A circular patch antenna is designed, simulated and tested to verify the accuracy of the far fields measured in the chamber, and a broadband horn antenna is measured to verify the accuracy of the near field measurements. The measured results for these two types of antennas matched the expected results, verifying the accuracy of each measurement type. Lastly, an antenna array is tested using both far field and near field systems, and the results from both measurement system were compared with each other. Good agreement is obtained, but due to the physical limitations related to the equipment used in the chamber and the size of the antenna some differences in the far field patterns are observed.Rights
Copyright of the original work is retained by the author.Collections
Related items
Showing items related by title, author, creator and subject.
-
Yampa coal field, Colorado: Moffat Tunnel will make field accessible: accompanied by chart (2 sheets): "Proximate analyses of samples of coal from the Yampa coal field, Moffat & Routt Counties, Colorado; all analyses made by the United States Bureau of Mines": 1924 Dept. of the Interior release 17848, TheEby, J. Brian (James Brian), 1896-; Geological Survey (U.S.); United States. Bureau of Mines
-
Field verification of stream-aquifer interactions: Colorado School of Mines survey field, Golden, ColoradoPoeter, Eileen; Anderman, Evan R. (Colorado School of Mines. Arthur Lakes Library, 1993)
-
Seismic and well log based machine learning facies classification in the Panoma-Hugoton field, Kansas and Raudhatain field, North KuwaitJin, Ge; Tura, Ali; Dwihusna, Nadima; Naeini, Ehsan; Sonnenberg, Stephen A.; TSvankin, I. D. (Colorado School of Mines. Arthur Lakes Library, 2020)This thesis focuses on applying machine learning on facies classification presented in three case studies: 1) supervised, 2) semi-supervised, and 3) unsupervised machine learning to classify facies in various well logs and seismic data in the Hugoton-Panoma Field, Kansas and Raudhatain Field, North Kuwait. The first study applies supervised machine learning to a set of labeled well logs from the Hugoton-Panoma Field, Kansas. The Hugoton-Panoma field is a stratigraphic trap overlying monocline, with a primary gas reservoir rock found in the Permian dolomite in the Chase and Council Grove group. The supervised machine learning algorithms consist of 2D Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, and Multilayer Perceptron (MLP). The algorithms classified the reservoir lithofacies sequences and updated the geologic interpretation of the dolomite target reservoir intervals. The supervised algorithms perform best with optimized hypoparameters and balanced training set. Supervised machine learning methods also tend to perform more accurately with more differential facies to classify. Once these supervised machine learning algorithms are fully optimized and trained, facies classification using machine learning is approaching the accuracy of traditional interpretation methods. The second and third case studies use unlabeled well log and post-stack seismic data to classify and characterize the facies variations in the deep (14,000 ft) Jurassic reservoirs of the Raudhatain Field, North Kuwait. The heterogeneous facies and strong seismic interbeded multiples affects the reservoir section. The reservoir interval is immediately below thick layers of Hith-Gotnia Formations with alternating salts and anhydrite, high-pressure high-temperature and sour fluid conditions provide geomechanical and environmental challenges. The Najmah Kerogen and Marrat formations are the main resource play development project in Kuwait, and the reservoir characterization is still uncertain. Hence, integrating machine learning to perform facies classification is essential to build a better understanding of subsurface conditions for further field development planning and exploration. The second case study involves semi-supervised learning for facies classification to the unlabeled well log data in the Raudhatain Field, North Kuwait. The K-Means unsupervised learning algorithm was trained and petrophysics domain knowledge was applied to label the classes. Combining petrophysics-based domain knowledge with machine learning allows the algorithm to be scalable to larger datasets, increase efficiency, and assist interpretation. Through this study, the reservoir characterization has been improved in the Upper Jurassic. Semi-supervised machine learning algorithm has classified the Hith-Gotnia interval of salt and anhydrites facies variation, and the Najmah to Marrat formations which contains the kerogen, siltstones, limestones, carbonates, and sandstone depositions. The third case study involves unsupervised machine learning for facies classification to the unlabeled post-stack seismic volume in the Raudhatain Field, North Kuwait. First, instantaneous and geometric attributes were generated from the post-stack seismic data. Through the Principal Component Analysis (PCA), a suitable combination of the attributes are selected. Afterwards, the Self-organizing Map (SOM) analysis was applied to identify the neurons or neural clusters visualized in a 2D Color Map. Each neuron in the 2D Color Map represents a cluster of data points. SOM is a good seismic visualization tool for interpreters to reveal additional information in the seismic data that may lead to geologic findings. In this study, facies recognition through SOM performed successfully in the Upper Jurassic, and limited success for the Lower Jurassic due to the complexity of the overburden and quality of the seismic data. In summary, machine learning improves the efficiency and aids the task of interpreters for exploring and characterizing facies in large volumes of well log and seismic data. All three case studies provides valuable insights in applying different types of machine learning for geologic interpretation of each fields. Facies recognition enabled by machine learning has high potential in the future of reservoir characterization.