[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117349-en":3,"doc-seo-117349-105":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},117349,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of Rock Types Using Machine Learning - Abstract","Petrophysical rock typing often omits measured multiphase flow properties such as relative permeability curves due to SCAL limitations and difficulties linking curve behavior to standard rock types. With a large dataset of relative permeability curves, machine learning can classify rock types automatically and objectively by curve shape. The workflow combines principal component analysis with unsupervised clustering, and preprocesses curves using irreducible water saturation and residual oil. Tests on carbonate reservoir data confirm clustering consistency and link clusters to displacement efficiency from poor to optimal, while emphasizing the need for high-quality SCAL experiments and noting informative value beyond capillary pressure analysis.","Classification of Rock Types Using Machine Learning  \nMarojahan Benedict Efrata*1, Widya Spalanzani2, Sekar Ayu Citrowati3  \n1,3Teknik Perminyakan, Fakultas Teknik Pertambangan dan Perminyakan, Institut Teknologi Bandung 2Teknik Industri, Fakultas Teknik, Universitas Bhayangkara Jakarta Raya, Indonesia  \ne-mail: *[1](1 22222302@mahasiswa.itb.ac.id)[ 22222302@mahasiswa.itb.ac.id](1 22222302@mahasiswa.itb.ac.id), [2](2widya.spalanzani@dsn.ubharajaya.ac.id)[widya.spalanzani@dsn.ubharajaya.ac.id](2widya.spalanzani@dsn.ubharajaya.ac.id),  \n[3](3sekaracitrowati@gmail.com)[sekaracitrowati@gmail.com](3sekaracitrowati@gmail.com)  \nAbstract  \nDetermining the petrophysical rock type often excludes measured multiphase flow properties, such as relative permeability curves. This is due to limitations in SCAL experiments or difficulties in correlating relative permeability characteristics with standard rock types. However, with a significant number of relative permeability curves, Machine Learning methods can be applied to automatically and objectively classify rock types based on the shape of these curves. This approach combines principal component analysis with unsupervised clustering schemes and preprocesses relative permeability curve data by integrating irreducible water saturation and residual oil. The methodology was tested on real data from carbonate reservoirs with a substantial number of relative permeability curves, demonstrating successful clustering based on fractional flow curves. The results indicate that this clustering can classify rocks from poor to optimal displacement efficiency. Furthermore, the study highlights the importance of high-quality SCAL experiments for normalizing curves and ensuring consistency between capillary pressure measurements and relative permeability. This Machine Learning approach is also compared with capillary pressure analysis, showing that relative permeability data provides additional information in rock typing studies, affirming the feasibility of Machine Learning for automatic rock type classification.  \nKeywords : Machine Learning, Relative Permeability, Rock Type Classification, Petrophysical Characterization, SCAL Experiments dan Fractional Flow Curves  \nAbstrak  \nPenentuan jenis batuan petrofisika seringkali tidak memasukkan sifat aliran multiphase yang diukur, seperti kurva permeabilitas relatif. Inidisebabkan oleh keterbatasan eksperimen SCAL atau kesulitan menghubungkan karakteristik permeabilitas relatif dengan jenisbatuan standar. Namun, dengan jumlah kurva permeabilitas relatif yang signifikan, metode Machine Learning dapat diterapkan untuk mengklasifikasikan jenis batuan berdasarkan bentuk kurva tersebut secara otomatis dan objektif. Pendekatan / Metode ini  \nmenggabungkan analisis komponen utamadengan skema klasterisasi tanpa pengawasan, serta pra-pemrosesan data kurva permeabilitas relatif dengan integrasi saturasi air irreducible dan minyak residual. Metodologi ini diuji pada data nyata dari reservoir karbonat denganjumlah kurva permeabilitas relatif yang signifikan, menunjukkan pengelompokan yang berhasil berdasarkan kurva aliran fraksional. Hasilnya menunjukkan bahwa pengelompokanini dapat mengklasifikasikan batuan dariefisiensi perpindahan yang buruk hinggaterbaik. Selain itu, hasil studi ini menyoroti pentingnya eksperimen SCAL berkualitas baikuntuk normalisasi kurva dan konsistensi pengambilan sampel antara pengukurantekanan kapiler dan permeabilitas relatif. Pendekatan / Metode Machine Learning ini juga dibandingkan dengan analisis tekanankapiler, menunjukkan bahwa data permeabilitas relatif membawa informasi tambahan dalam studi pengelompokan jenis batuan, menegaskan kelayakan proses Machine Learning untukdefinisi jenis batuan secara otomatis  \nKata Kunci: Machine Learning, Permeabilitas Relatif, Klasifikasi Jenis Batuan, Karakterisasi Petrofisika, SCAL Experiments dan Fractional Flow Curves.  \nPENDAHULUAN  \nJenis batuan mengacu pada berbagai kategori batuan berdasarkan ko","cbCaiiwYC6fv3Ey9","https://ap.wps.com/l/cbCaiiwYC6fv3Ey9","pdf",833604,1,10,"English","en",105,"# PENDAHULUAN\n## Rock types and petrophysical context\n## General machine learning workflow for rock classification","[{\"question\":\"Mengapa penentuan rock type petrofisika sering mengabaikan kurva permeabilitas relatif?\",\"answer\":\"Karena keterbatasan eksperimen SCAL dan kesulitan menghubungkan karakteristik permeabilitas relatif dengan jenisbatuan standar.\"},{\"question\":\"Metode machine learning apa yang digunakan untuk mengklasifikasikan rock types berdasarkan kurva permeabilitas relatif?\",\"answer\":\"Metodologi menggabungkan principal component analysis dengan skema klasterisasi tanpa pengawasan, serta pra-pemrosesan kurva dengan integrasi saturasi air irreducible dan minyak residual.\"},{\"question\":\"Apa temuan utama dari pengujian pada data reservoir karbonat?\",\"answer\":\"Pengelompokan berhasil berdasarkan kurva aliran fraksional dan dapat mengklasifikasikan batuan dari efisiensi perpindahan yang buruk hingga terbaik, sekaligus menekankan pentingnya SCAL berkualitas baik.\"}]","Classification of Rock Types Using Machine Learning - Abstract | PDF",1785675307,25,{"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},"classification-of-rock-types-using-machine-learning-abstract","",{"@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/classification-of-rock-types-using-machine-learning-abstract/117349/",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},"Mengapa penentuan rock type petrofisika sering mengabaikan kurva permeabilitas relatif?","Question",{"text":75,"@type":76},"Karena keterbatasan eksperimen SCAL dan kesulitan menghubungkan karakteristik permeabilitas relatif dengan jenisbatuan standar.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Metode machine learning apa yang digunakan untuk mengklasifikasikan rock types berdasarkan kurva permeabilitas relatif?",{"text":80,"@type":76},"Metodologi menggabungkan principal component analysis dengan skema klasterisasi tanpa pengawasan, serta pra-pemrosesan kurva dengan integrasi saturasi air irreducible dan minyak residual.",{"name":82,"@type":73,"acceptedAnswer":83},"Apa temuan utama dari pengujian pada data reservoir karbonat?",{"text":84,"@type":76},"Pengelompokan berhasil berdasarkan kurva aliran fraksional dan dapat mengklasifikasikan batuan dari efisiensi perpindahan yang buruk hingga terbaik, sekaligus menekankan pentingnya SCAL berkualitas baik.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]