[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118635-en":3,"doc-seo-118635-105":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},118635,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques","Machine learning has been increasingly applied to geoscience problems, yet its use for reservoir characterization remains at an early stage with substantial room for development. This research targets the Late Permian Beekeeper Formation from the Perth Basin, Australia, aiming to improve understanding of how machine learning can characterize subsurface rock formations. The study performs cutting, crossplot, and modern machine learning analyses, compares their outputs, and evaluates classification accuracy for electrofacies and lithofacies. Seven electrofacies linked to nine lithofacies are identified and grouped into carbonate-dominated, siliciclastic-dominated, and mixed carbonate-siliciclastic facies, showing stratal and compositional mixing.","JURNAL NATURAL  \nVol. 25,(3) 2025  \nDOI 10.24815/jn.v25i3 .47470  \npISSN 1411-8513 eISSN 2541-4062  \nORIGINAL RESEARCH  \nElectrofacies classification of a mixed carbonate  \nsiliciclastic reservoir using machine learning techniques  \nMUHAMMAD RIDHA ADHARI1*, FREDDY SAPTA WIRANDHA2, MUHAMMAD YANIS2, MUHAMMAD YUSUF KARDAWI3  \n1Department of Geological Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia, 23111  \n2Department of Geophysical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia, 23111  \n3Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia, 16424  \n*Corresponding Author: [mr.adhari@usk.ac.id](mr.adhari@usk.ac.id)  \nReceived: 30 June 2025  \nRevised: 18 September 2025  \nAccepted: 22 September 2025  \nAbstract. Many scientific fields, including the geosciences, have successfully employed machine learning to address numerous significant issues. Current studies show that the application of machine learning within the geosciences is still in its early stages, and thereis a huge potential for this technique that need to be explored. This research focuses on the Late Permian Beekeeper Formation from the Perth Basin, Australia. It aims to improve our understanding of the application of machine learning to characterise subsurface rock formations. The objectives ofthis study are threefold: (1) to conduct cutting, crossplot, and modern machine learning analyses on a mixed carbonate-siliciclastic reservoir; (2) to compare the results from the aforementioned analyses and to interpret the electrofacies and lithofacies; and (3) to understand the degree of accuracy of the application of machine learning in the characterisation of the subsurface rock formations. Cutting, crossplotting, and modern machine learning analyses have been conducted to achieve the aim and objectives ofthis study. Seven electrofacies, associated with nine lithofacies, were identified within the studied data, and these were classified into carbonate-dominated facies group, siliciclastic-dominated facies group, and mixed carbonate-siliciclastic facies group. Results also show the presence of stratal and compositional mixing within the Beekeeper Formation. A combination of cutting, crossplot, and machine learning analyses can provide a better, more accurate, and more reliable interpretation of the facies of the Beekeeper Formation. This study is expected to advance our understanding of the application of machine learning in geosciences.  \nKeywords: Beekeeper Formation; Electrofacies; Machine Learning; Mixed CarbonateSiliciclastic reservoir.  \nINTRODUCTION  \nMachine learning has transformed our modern society and greatly impacted our culture. Many fields of science, including computer sciences, engineering, education, healthcare and medical sciences, accounting and finance, business and marketing, agriculture, and geosciences, have now used machine learning to help tackle many issues related to their particular field [1, 2] . Within the geosciences, this relatively new approach has successfully been applied to solve various problems such as slope failure prediction, and geohazard modelling [3- 5], seismic processing and interpretation [6, 7], subsurface formation evaluation [8, 9], hydrocarbon exploration and production [10-12], seismology and  \nearthquake sciences [13, 14], and mineral prospectivity mapping [15-17] . Despite all these advancements, the application of machine learning in geosciences is still considered in its infancy, with many opportunities to develop and expand its application in geosciences.  \nElectrofacies, defined as \"the set of log responses that characterise a bed and permit it to be distinguished from others\" [18], are commonly studied in hydrocarbon exploration to better understand subsurface rock formations. In the past several decades, many geoscientists have relied on the traditional crossplot of the wireline log data following the method by Schlumberger [19, ","cbCaioaXSqQuGU5U","https://ap.wps.com/l/cbCaioaXSqQuGU5U","pdf",2404254,1,12,"English","en",105,"# Introduction\n## Electrofacies and traditional crossplot approach\n## Motivation for machine learning in geosciences","[{\"question\":\"What geologic target and setting does the study examine?\",\"answer\":\"The study examines the Late Permian Beekeeper Formation from the Perth Basin, Australia, using available subsurface data from Woodada-14 and East Lake Logue-1.\"},{\"question\":\"Which analyses are used to classify electrofacies and lithofacies?\",\"answer\":\"The study uses cutting, crossplot, and modern machine learning analyses, then compares their results to interpret electrofacies and lithofacies.\"},{\"question\":\"What facies groups and mixing evidence are reported?\",\"answer\":\"Seven electrofacies associated with nine lithofacies are grouped into carbonate-dominated, siliciclastic-dominated, and mixed carbonate-siliciclastic facies. The results indicate stratal and compositional mixing within the Beekeeper Formation.\"}]","Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques | PDF",1785684627,30,{"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},"electrofacies-classification-of-a-mixed-carbonate-siliciclastic-reservoir-using-machine-learning-techniques","",{"@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/electrofacies-classification-of-a-mixed-carbonate-siliciclastic-reservoir-using-machine-learning-techniques/118635/",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","2026-08-02",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},"What geologic target and setting does the study examine?","Question",{"text":75,"@type":76},"The study examines the Late Permian Beekeeper Formation from the Perth Basin, Australia, using available subsurface data from Woodada-14 and East Lake Logue-1.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which analyses are used to classify electrofacies and lithofacies?",{"text":80,"@type":76},"The study uses cutting, crossplot, and modern machine learning analyses, then compares their results to interpret electrofacies and lithofacies.",{"name":82,"@type":73,"acceptedAnswer":83},"What facies groups and mixing evidence are reported?",{"text":84,"@type":76},"Seven electrofacies associated with nine lithofacies are grouped into carbonate-dominated, siliciclastic-dominated, and mixed carbonate-siliciclastic facies. 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