[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290984-105":59,"doc-detail-290984-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","automated-real-time-prediction-of-geological-formation-tops-during-drilling-operations-an-applied-machine-learning-solution-for-the-norwegian-continental-shelf","Automated real-time prediction of geological formation tops during drilling operations - an applied machine learning solution for the Norwegian Continental Shelf","","Accurate prediction of geological formation tops is essential for optimizing hydrocarbon exploration and production, especially in the complex Norwegian Continental Shelf (NCS). The study compares four machine learning models—SVM, KNN, Random Forest, and an MLP neural network—using a dedicated test dataset and an independent blind validation dataset. It evaluates predictive accuracy across multiple formation top types and benchmarks performance against a DBSCAN baseline clustering approach. Feature importance is analyzed with PFI and SHAP, identifying depth (DEPT), RPM, and Hook-load (HKLD) as key drivers. The MLP achieves 0.99 on blind validation, while SVM reaches 0.99 on the initial test dataset; further validation on larger datasets is required for generalizability.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-real-time-prediction-of-geological-formation-tops-during-drilling-operations-an-applied-machine-learning-solution-for-the-norwegian-continental-shelf/290984/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-real-time-prediction-of-geological-formation-tops-during-drilling-operations-an-applied-machine-learning-solution-for-the-norwegian-continental-shelf/290984.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which machine learning models are compared for geological formation tops prediction?","Question",{"text":112,"@type":113},"The research benchmarks Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, and a Multi-Layer Perceptron (MLP) neural network.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are model performances evaluated in the study?",{"text":117,"@type":113},"Models are tested on a dedicated test dataset and an independent blind validation dataset, with evaluation focused on predictive accuracy for classifying multiple formation top types.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the feature-importance analyses (PFI and SHAP) identify as most influential?",{"text":121,"@type":113},"PFI and SHAP consistently highlight depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters across different algorithms.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290984,1789643117,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","Journal of Petroleum Exploration and Production Technology (2024) 14:1661–1703  \n[https://doi.org/10.1007/s13202-024-01789-5](https://doi.org/10.1007/s13202-024-01789-5)  \nAutomated real‑time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf  \nBehzad Elahifar1 · Erfan Hosseini2  \nReceived: 17 December 2023 / Accepted: 11 March 2024 / Published online: 8 April 2024 © The Author(s) 2024  \nAbstract  \nAccurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets-a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques-Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model asthe top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS.  \n* Behzad Elahifar [behzad.elahifar@ntnu.no](behzad.elahifar@ntnu.no)  \n* Erfan Hosseini[e.hoseini@niocexp.ir](e.hoseini@niocexp.ir)  \n1 Department of Geoscience and Petroleum, Norwegian University of Science and Technology (NTNU), Trondheim, Norway  \n2 Exploration Directorate, National Iranian Oil Company (NIOC), Tehran, Iran  \nGraphical abstract  \nKeywords Geological formation tops prediction · Machine learning · Multi-layer perceptron · Support vector machine · K-nearest neighbors · Random forest · Density-based spatial clustering of applications with noise  \nAbbreviations  \nDEPT Depth: bit dep","cbCaihQdbpdnWyDg","https://ap.wps.com/l/cbCaihQdbpdnWyDg","pdf",12659976,43,"English","# Abstract\n## Comparative machine learning models\n## Datasets and evaluation metrics\n## Baseline DBSCAN clustering\n## Feature importance (PFI, SHAP)\n## Key findings and implications","[{\"question\":\"Which machine learning models are compared for geological formation tops prediction?\",\"answer\":\"The research benchmarks Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, and a Multi-Layer Perceptron (MLP) neural network.\"},{\"question\":\"How are model performances evaluated in the study?\",\"answer\":\"Models are tested on a dedicated test dataset and an independent blind validation dataset, with evaluation focused on predictive accuracy for classifying multiple formation top types.\"},{\"question\":\"What do the feature-importance analyses (PFI and SHAP) identify as most influential?\",\"answer\":\"PFI and SHAP consistently highlight depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters across different algorithms.\"}]","Automated real-time prediction of geological formation tops during drilling operations - an applied machine learning solution for the Norwegian Continental Shelf | PDF",108]