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The study presents an image-based supervised workflow that converts multi-feature well-log inputs—Gamma Ray, Density, Neutron Porosity, Photoelectric Effect, interpreted facies, TVDSS, and spatial distance cues—into labeled 2D windows for CNN training and blind-well evaluation in the Shahd SE field. Results show high prediction accuracy and improved generalization across wells, using pseudo-image encoding, explicit inter-well distance for geological continuity, and post-processing filtering to refine zone boundaries.",{"@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/convolutional-neural-network-approach-for-automated-well-zonation-in-the-lower-bahariya-member-north/455648/",{"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/convolutional-neural-network-approach-for-automated-well-zonation-in-the-lower-bahariya-member-north/455648.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in the Lower Bahariya member?","Question",{"text":112,"@type":113},"It addresses difficulties in reliable well-to-well correlation and zonation caused by complex stacked sand-channel architecture, similar petrophysical responses, and thin or laterally discontinuous shale barriers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the workflow convert well-log data for CNN training?",{"text":117,"@type":113},"It transforms multiple well-log features into labeled images, where each image is a normalized vertical window of subsurface data aligned to expert-interpreted stratigraphic zones for supervised CNN classification.",{"name":119,"@type":110,"acceptedAnswer":120},"What new elements distinguish the proposed CNN approach from prior studies?",{"text":121,"@type":113},"It uses optimized pseudo-image encoding, integrates inter-well spatial distance as an explicit feature to enhance geological continuity, and applies post-processing filtering to refine zone boundaries.","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},455648,1791366151,{"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":14,"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":144,"read_time":145},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nConvolutional neural network approach for automated well zonation in the Lower Bahariya member northWestern Desert Egypt  \nKhaled Saleh1,2􀀍, Walid M. Mabrouk1 & Ahmed M. Metwally1  \nAccurate well-to-well stratigraphic zonation is fundamental to subsurface reservoir characterization, particularly in geologically complex settings such as tidal channel systems where lithological variability and discontinuous shale barriers pose significant interpretation challenges. This study introduces a novel, image-based workflow leveraging Convolutional Neural Networks (CNNs) to automate zonation in newly drilled wells using conventional well log data. The proposed approach transforms multiple input features—including Gamma Ray (GR), Density (RHOB), Neutron Porosity (NPHI), Photoelectric Effect (PE), interpreted facies, True Vertical Depth Subsea (TVDSS), and three spatial distance features—into images. Each image represents a normalized vertical window of subsurface data and is labeled according to expert-interpreted stratigraphic zones. These labeled images form the training dataset for a supervised CNN classification model. The methodology is applied to the Shahd SE field in the northern Western Desert of Egypt, targeting the Lower Bahariya member. The trained CNN model is evaluated on blind wells demonstrating high prediction accuracy and successful generalization across wells. The proposed workflow introduces a novel image-based transformation of 1Dwell-log sequences into 2D representations that enables the use of convolutional neural networks traditionally designed for spatial image analysis. Unlike previous zonation studies that rely on raw 1D signals or statistical clustering, our approach (i) applies an optimized pseudo-image encoding of log data,(ii) integrates inter-well spatial distance as an explicit feature to enhance geological continuity, and (iii) incorporates post-processing filtering to refine zone boundaries. This combination provides a more robust and automated zonation methodology with improved generalization across blind wells.  \nKeywords Well correlation, Zonation, Convolutional neural network, Lower Bahariya  \nWell-log correlation is a fundamental step in subsurface geological interpretation and reservoir characterization. It involves aligning lithological boundaries across multiple wells to construct a continuous stratigraphic framework, essential for geological modeling and predicting lateral variations in lithology1,2. This step is particularly critical in heterogeneous depositional settings, such as fluvial and tidal systems, where rapid facies changes and discontinuous shale barriers can significantly influence reservoir connectivity and fluid flow behavior. In both static and dynamic reservoir models, accurate correlation directly impacts reservoir compartmentalization and the reliability of simulation3,4.  \nIn the Shahd SE Field, one of the key challenges lies in establishing reliable well-to-well correlations within the Lower Bahariya member. This formation exhibits a complex architecture, composed of multiple stacked sand channels that often share similar petrophysical log responses—such as gamma ray, density, and porosity—making it difficult to delineate individual zones using traditional manual methods5. The challenge is further intensified by the presence of thin or laterally discontinuous shale barriers, which are difficult to resolve yet critically control fluid flow and compartmentalization. Misinterpretation of these features can lead to overestimated connectivity and poor history matching during reservoir simulation. Recent studies have highlighted the significant influence  \n1Department of Geophysics, Faculty of Science, Cairo University, Giza 12613, Egypt. 2PetroShahd Company, Zahraa Maadi, Cairo, Egypt. 􀀍 email: [khaledsaleh@gstd.sci.cu.edu.eg](khaledsaleh@gstd.sci.cu.edu.eg)  \n[www. nature.com/scientificreports/](www.","cbCaio9ZK8mLgksi","https://ap.wps.com/l/cbCaio9ZK8mLgksi","pdf",4321492,18,"English","# Introduction\n## Background and importance of well-log correlation\n## Challenges in Lower Bahariya member zonation\n## Related work and machine-learning advances\n# Proposed methodology\n## Image-based CNN zonation workflow\n## Pseudo-image encoding and spatial-distance feature\n## Post-processing for zone-boundary refinement\n# Application and evaluation\n## Case study: Shahd SE field (Northern Western Desert, Egypt)\n## Blind-well testing and generalization","[{\"question\":\"What problem does the study address in the Lower Bahariya member?\",\"answer\":\"It addresses difficulties in reliable well-to-well correlation and zonation caused by complex stacked sand-channel architecture, similar petrophysical responses, and thin or laterally discontinuous shale barriers.\"},{\"question\":\"How does the workflow convert well-log data for CNN training?\",\"answer\":\"It transforms multiple well-log features into labeled images, where each image is a normalized vertical window of subsurface data aligned to expert-interpreted stratigraphic zones for supervised CNN classification.\"},{\"question\":\"What new elements distinguish the proposed CNN approach from prior studies?\",\"answer\":\"It uses optimized pseudo-image encoding, integrates inter-well spatial distance as an explicit feature to enhance geological continuity, and applies post-processing filtering to refine zone boundaries.\"}]","Convolutional neural network approach for automated well zonation in the Lower Bahariya member north | PDF",1790743750,45]