[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125702-en":3,"doc-seo-125702-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125702,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning based seismic classification for facies prediction - Master’s thesis","This master’s thesis evaluates machine learning approaches for predicting facies from seismic attributes for both 2D and 3D datasets. The study builds, trains, and tests four supervised methods—Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and Random Forest—alongside a deep learning model, a neural network with two hidden layers. A realistic synthetic facies model with complex depositional systems and a synthetic seismic cube enable comparisons against ground-truth facies distributions, validating prediction using wells and seismic. The work analyzes how the number and location of wells, seismic data frequency, and selected seismic attributes influence accuracy. Seismic inversion and relative acoustic impedance emerge as the most important features, while instantaneous frequency and envelope have limited impact; adding lateral facies geometry improves results.","| \u003Cbr>FACULTY OF SCIENCE AND TECHNOLOGY\u003Cbr>MASTER’S THESIS |  |\n| --- | --- |\n| Study programme / specialisation: Petroleum Geosciences Engineering | The spring semester, 2023 Open |\n| Author: Aigul Taimour Alvi |  |\n| Supervisor at UiS: Nestor Cardozo\u003Cbr>Co-supervisor:\u003Cbr>External supervisor(s): Lothar Schulte (SLB) |  |\n| Thesis title: Machine learning based seismic classification for facies prediction |  |\n| Credits (ECTS): 30 |  |\n| Keywords:\u003Cbr>Machine learning, facies,\u003Cbr>synthetic models, 2D facies prediction, 3D facies prediction, seismic attributes, seismic inversion | Pages: 112\u003Cbr>+ appendix: 31\u003Cbr>Stavanger, 14.06.2023 |\n\nMachine learning based seismic classification for  \nfacies prediction  \nby  \nAigul TaimourAlvi  \nMSc Thesis  \nStavanger, June 2023 University of Stavanger  \nFaculty of Science and Technology Department of Energy Resources  \nAcknowledgements  \nI would like to express my deepest gratitude to my supervisor Professor Nestor Cardozo for his unconditional support, advise and coordination throughout writing the thesis. Special thanks to my external supervisor Doctor Lothar Schulte for his encouragement, patience and guidance from generating ideas to performing calculations and discussing results.  \nI would also like to thank my family for supporting and believing in me along this journey.  \nAbstract  \nThis thesis explores the performance of machine learning (ML) methods for predicting facies from seismic attributes for 2D and 3D datasets. It focuses on building, training, and testing four supervised methods: Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and Random Forest; and one deep learning method: Neural Network with two hidden layers. A realistic synthetic facies model with complex depositional systems, and a synthetic seismic cube from the facies model are used for the comparison of facies prediction performed by the ML approach with the ground-truth facies distribution. This comparison makes it possible to validate the ML models’ prediction based on wells and seismic. In addition, the research evaluates the role of the number of wells and their locations, the impact of seismic data frequency, and the effect of using various seismic attributes. The most important features for facies prediction are seismic inversion and relative acoustic impedance. Instantaneous frequency and envelope have little effect on the accuracy of the ML prediction. Incorporating information about the lateral geometry of the facies in the reservoir also improves the accuracy of the ML prediction.  \nTable of Contents  \n1.Introduction, Objectives, and Thesis Structure .......................................................................... 14  \n1.1 Introduction .................................................................................................................... 14  \n2. Dataset description ................................................................................................................ 17  \n2.1 3D facies cube and its sections....................................................................................... 17  \n2.2 The description of the dataset generation and seismic attributes ................................... 20  \n3 Machine Learning Theoretical Background .......................................................................... 25  \n3.1 Supervised ML methods (binary, multi-class, regression) ............................................. 25  \n3.1.1 Logistic Regression................................................................................................. 27  \n3.1.2 Support Vector Machines ....................................................................................... 28  \n3.1.2 K-Nearest Neighbor ................................................................................................ 30  \n3.1.3 Random Forest ........................................................................................................ 31  \n3.2 Neural Networks .....","cbCaihVv9mOW0eOJ","https://ap.wps.com/l/cbCaihVv9mOW0eOJ","pdf",8052630,1,143,"English","en",105,"# Acknowledgements\n# Abstract\n# 1. Introduction, Objectives, and Thesis Structure\n## 1.1 Introduction\n# 2. Dataset description\n## 2.1 3D facies cube and its sections\n## 2.2 The description of the dataset generation and seismic attributes\n# 3. Machine Learning Theoretical Background\n## 3.1 Supervised ML methods (binary, multi-class, regression)\n## 3.2 Neural Networks\n# 4. Methodology\n## 4.1 Dataset preparation and analysis workflow\n## 4.2 Machine learning workflow\n# 5. Data Analysis and Processing\n## 5.1 Exploratory facies analysis\n## 5.2 Exploratory features analysis","[{\"question\":\"Which machine learning methods are used for facies prediction?\",\"answer\":\"The thesis tests four supervised methods—Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and Random Forest—and one deep learning approach, a neural network with two hidden layers.\"},{\"question\":\"How is the model validation performed?\",\"answer\":\"Predictions are compared with ground-truth facies distributions from a realistic synthetic facies model and a synthetic seismic cube, enabling validation based on wells and seismic.\"},{\"question\":\"What factors most affect prediction accuracy?\",\"answer\":\"The most important features are seismic inversion and relative acoustic impedance. Accuracy also improves when lateral geometry information of the reservoir facies is incorporated; instantaneous frequency and envelope have little effect.\"}]","Machine learning based seismic classification for facies prediction - Master’s thesis | PDF",1785900734,360,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-seismic-classification-for-facies-prediction-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-seismic-classification-for-facies-prediction-masters-thesis/125702/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning methods are used for facies prediction?","Question",{"text":75,"@type":76},"The thesis tests four supervised methods—Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and Random Forest—and one deep learning approach, a neural network with two hidden layers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the model validation performed?",{"text":80,"@type":76},"Predictions are compared with ground-truth facies distributions from a realistic synthetic facies model and a synthetic seismic cube, enabling validation based on wells and seismic.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors most affect prediction accuracy?",{"text":84,"@type":76},"The most important features are seismic inversion and relative acoustic impedance. Accuracy also improves when lateral geometry information of the reservoir facies is incorporated; instantaneous frequency and envelope have little effect.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]