[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118963-en":3,"doc-seo-118963-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118963,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","USE OF IMAGE-BASED MACHINE LEARNING AND QUANTUM LEARNING FOR SUBSURFACE CHARACTERIZATION - A Thesis","This thesis investigates machine-learning methods for subsurface characterization, proposing an image-based, data-driven workflow to estimate oil viscosity from side-wall rock sample images. Image features are extracted using multiple image-based filters and statistical models, enabling both regression and classification; the classification results are promising, while regression performance requires further improvement. Subsequent chapters evaluate quantum-enhanced machine learning for lithology classification using well-log data and compare it with classical approaches. Experiments with varying sample sizes and iterative runs assess whether quantum advantage emerges under limited data conditions.","USE OF IMAGE-BASED MACHINE LEARNING AND QUANTUM LEARNING FOR  \nSUBSURFACE CHARACTERIZATION  \nA Thesis  \nby  \nMATTEO CAPONI  \nSubmitted to the Graduate and Professional School of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nChair of Committee, Committee Members,  \nHead of Department,  \nSiddharth Misra Hadi Nasrabadi David W. Bapst Jeff Spath  \nDecember 2022  \nMajor Subject: Petroleum Engineering Copyright 2022 Matteo Caponi  \nABSTRACT  \nThe proposed thesis aims to explore novel applications of machine learning for subsurface characterization. In the first chapter, an image-based data-driven workflow is proposed to characterize oil viscosity from side-wall rock sample images. Informative features are extracted from the rock sample images deploying several image-based filters and statistical models. Both regression and classification tasks are performed on thepreprocessed data. The proposed workflow shows promising results for viscosity classification whereas future work is needed to improve the regression performance. The second and third chapters explore the application of quantumenhanced machine learning models for lithology classification and the resulting comparison with classical machine learning models. The second chapter compares a quantum support vector machine with a traditional support vector classifier for lithology classification from well log data. Different sample sizes are tested to understand if a quantum advantage is obtained when the available data is limited. The third chapter investigates the application of both quantum support vector and variational quantum classifier for binary lithology classification. The score distribution obtained from testing the models with multiple iterations gives more insight on the current performance capabilities of quantum-enhanced machine models when compared to artificial networks. Overall, although a quantum advantage is not observed in both chapters, this work opens the door to future applications of quantum-enhanced machine learning for subsurface characterization.  \nACKNOWLEDGEMENTS  \nI would like to thank my committee chair, Dr. Misra, and my committee members, Dr. Nasrabadi, and Dr. Bapst, for their guidance and support throughout the course of this research. In addition, I would like to acknowledge Adam Cox and the entire team at Berry Petroleum for their guidance and responsiveness in addition to my research teammates Rui Liu and Yusuf Falola for improving my knowledge of data science. Thanks also go to my classmates, the department faculty, and staff for making my time at Texas A&M University a great experience. Finally, thanks to my mother and father for providing me constant encouragement and the tools to achieve my dreams.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supervised by a thesis committee consisting of Professor Dr. Siddharth Misra and Dr. Hadi Nasrabadi of the Department of Petroleum Engineering and Professor Dr. David W. Bapst of the Department of Geology & Geophysics.  \nFunding Sources  \nGraduate study was supported by a fellowship and research assistantship from the DICE research group led by Dr. Misra and the Department of Petroleum Engineering at Texas A&M University. The contents and results of this work are solely the responsibility of the student and his advisory committee, and do not necessarily represent the official views of the funding sources cited above.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT.............................................................................................................................. ii  \nACKNOWLEDGEMENTS ...................................................................................................... iii  \nCONTRIBUTORS AND FUNDING SOURCES .................................................................... iv  \nTABLE OF CONTENTS...............................................................................................","cbCaiiMP7iFtGWSc","https://ap.wps.com/l/cbCaiiMP7iFtGWSc","pdf",3113899,1,123,"English","en",105,"# ABSTRACT\n# CHAPTER I IMAGE PREPROCESSING AND SUPERVISED LEARNING FOR VISCOSITY PREDICTION IN HEAVY OIL RESERVOIRS\n## Fundamental Questions\n## Novelty and Scientific Impact\n## Introduction\n## Literature Review\n## Background\n## Machine Learning Models\n## Performance Metrics\n## Regression","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To explore novel applications of machine learning for subsurface characterization, including both classical image-based methods and quantum-enhanced models.\"},{\"question\":\"How is oil viscosity characterized in Chapter I?\",\"answer\":\"By extracting informative features from side-wall rock sample images using image-based filters and statistical models, then applying regression and classification tasks.\"},{\"question\":\"What do the quantum learning chapters focus on?\",\"answer\":\"Quantum-enhanced machine learning models are applied to lithology classification, including comparisons between quantum and classical support vector methods and evaluation of binary lithology classification.\"}]","USE OF IMAGE-BASED MACHINE LEARNING AND QUANTUM LEARNING FOR SUBSURFACE CHARACTERIZATION - 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