[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124495-id":3,"doc-seo-124495-113":31,"detail-sidebar-cat-0-id-113":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},124495,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",54,"Penelitian & Laporan","STUDI PREDIKSI TIPE REKAHAN PADA RESERVOIR VULKANIK JATIBARANG LAPANGAN “TGB” DENGAN METODE MACHINE LEARNING","Reservoir vulkanik dicirikan oleh heterogenitas tinggi akibat proses geologi kompleks seperti aliran lava, pengendapan tuf, dan alterasi hidrotermal. Keberadaan rekahan alam menjadi faktor kunci karena memengaruhi permeabilitas serta produktivitas fluida. Penentuan tipe rekahan secara konvensional melalui interpretasi borehole image dan analisis core bersifat subjektif, memakan waktu, serta mahal. Penelitian ini mengusulkan pendekatan machine learning untuk memprediksi tipe rekahan pada Reservoir Vulkanik Jatibarang menggunakan data log dan well image log. Model diuji dengan akurasi, precision, recall, dan F1-score; Random Forest memberikan kinerja terbaik. Fitur paling berpengaruh ialah DEPTH, GR, ILD, NPHI, dan DT. Pendekatan ini mendukung evaluasi reservoir dan keputusan pengembangan lapangan secara lebih cepat dan akurat.","E-ISSN : 2614-7297, Volume 14 Nomor 4, Desember 2025, Halaman 320-331  \nPETRO: JURNAL ILMIAH TEKNIK PERMINYAKAN  \n[https://e-journal.trisakti.ac.id/index.php/petro](https://e-journal.trisakti.ac.id/index.php/petro)  \nSTUDI PREDIKSI TIPE REKAHAN PADA RESERVOIR VULKANIK JATIBARANG LAPANGAN “TGB” DENGAN METODE MACHINE LEARNING  \nBabas Samudera Hafwandi1  \n1Program Studi Teknik Perminyakan, Fakultas Teknik, Universitas Jember  \n*[Penulis Korespondensi: ](Penulis Korespondensi: babashafwandi@unej.ac.id)[babashafwandi@unej.ac.id](Penulis Korespondensi: babashafwandi@unej.ac.id)  \nAbstrak  \nReservoir vulkanik dikenal memiliki heterogenitas yang tinggi akibat aktivitas geologi kompleksseperti aliran lava, pengendapan tuf, serta proses alterasi hidrotermal. Salah satu karakteristik penting dalam reservoir ini adalah keberadaan rekahan alam, yang secara langsung memengaruhi permeabilitas dan produktivitas fluida. Rekahan dapat terbentuk secara alami maupun akibatproses tektonik dan diklasifikasikan menjadi beberapa tipe, seperti rekahan konduktif (conductive), rekahan tertutup (sealed), dan rekahan campuran (mixed) . Penentuan tipe rekahan secarakonvensional memerlukan interpretasi dari log citra borehole dan analisis deskripsi core, yang bersifat subjektif, memakan waktu, dan mahal. Penelitian ini bertujuan untuk mengembangkan pendekatan berbasis machine learning untuk memprediksi tipe rekahan pada Reservoir Vulkanik Jatibarang menggunakan data log dan data Well Image Log. Lima algoritma digunakan: Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, Support Vector Machine (SVM), dan KNearest Neighbor (KNN) . Validasi dilakukan menggunakan akurasi, precision, recall dan F1-score. Hasil menunjukkan bahwa algoritma Random Forest menghasilkan akurasi tertinggi dengan nilai Accuracy sebesar 0.8678, Precision sebesar 0.8678, Recall sebesar 0.8870 dan F1 sebesar 0.8756 (89,2%), pada data Blind Testing. dengan fitur paling penting adalah DEPTH, GR, ILD, NPHI, dan DT. Studi ini membuktikan bahwa machine learning dapat digunakan sebagai metode alternatif yang cepat dan akurat dalam klasifikasi rekahan, membantu proses evaluasi reservoir dan pengambilan keputusan pengembangan lapangan.  \nAbstract  \nVolcanic reservoirs are characterized by high heterogeneity resulting from complex geological processes such as lava flows, tuff deposition, and hydrothermal alteration. A key feature of these reservoirs is the presence of natural fractures, which directly influence permeability and fluid productivity. Fractures may develop naturally or due to tectonic processes and are commonly classified into conductive, sealed, and mixed types. Traditionally, fracture type identification relies on borehole image log interpretation and core description analysis, which are often subjective, time-consuming, and costly. This study proposes a machine learning-based approach to predict fracture types in the “X” Volcanic Reservoir using well log data (GR, RHOB, DT, NPHI, FMI) and interpreted core data. Five algorithms were employed: Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Model performance was evaluated using accuracy, precision, recall, and F1-score. The Random Forest algorithm achieved the best results, with an accuracy of 0.8678, precision of 0.8678, recall of 0.8870, and F1-score of 0.8756 (89.2%) on blind testing data. The most influential features were DEPTH, GR, ILD, NPHI, and DT. The findings indicate that machine learning can serve as a reliable and efficient alternative for fracture classification, offering improved accuracy and speed compared to conventional methods. This approach provides valuable support for reservoir evaluation and field development decision-making.  \nSejarah Artikel  \n• Diterima Oktober 2025  \n• Revisi November 2025  \n• Disetujui November 2025  \n• Terbit Online Desember 2025  \nKata Kunci:  \n• Machine Learning  \n• Tipe Pori  \n• Reservoir Vulkanik  \n•","cbCaiqisCF6FknKs","https://ap.wps.com/l/cbCaiqisCF6FknKs","pdf",1414540,3,1,12,"Indonesian","id",113,"# I. PENDAHULUAN\n## Latar belakang dan pentingnya rekahan pada reservoir vulkanik\n## Keterbatasan metode konvensional interpretasi borehole image dan core\n## Tujuan dan gagasan penggunaan machine learning","[{\"question\":\"Mengapa tipe rekahan penting dalam reservoir vulkanik?\",\"answer\":\"Rekahan memengaruhi jalur aliran fluida sehingga berdampak pada permeabilitas dan porositas sekunder, yang kemudian memengaruhi estimasi cadangan serta strategi produksi.\"},{\"question\":\"Apa keterbatasan metode konvensional dalam penentuan tipe rekahan?\",\"answer\":\"Interpretasi borehole image dan analisis core bersifat subjektif, memakan waktu, serta berbiaya tinggi.\"},{\"question\":\"Algoritma apa yang menghasilkan performa terbaik dalam penelitian ini?\",\"answer\":\"Random Forest menghasilkan akurasi tertinggi pada blind testing dengan Accuracy 0.8678 dan F1-score 0.8756 (89,2%).\"}]","STUDI PREDIKSI TIPE REKAHAN PADA RESERVOIR VULKANIK JATIBARANG LAPANGAN “TGB” DENGAN METODE MACHINE LEARNING | PDF",1785822761,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prediction-of-fracture-type-in-the-jatibarang-volcanic-reservoir-tgb-field-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/prediction-of-fracture-type-in-the-jatibarang-volcanic-reservoir-tgb-field-using-machine-learning/124495/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa tipe rekahan penting dalam reservoir vulkanik?","Question",{"text":76,"@type":77},"Rekahan memengaruhi jalur aliran fluida sehingga berdampak pada permeabilitas dan porositas sekunder, yang kemudian memengaruhi estimasi cadangan serta strategi produksi.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Apa keterbatasan metode konvensional dalam penentuan tipe rekahan?",{"text":81,"@type":77},"Interpretasi borehole image dan analisis core bersifat subjektif, memakan waktu, serta berbiaya tinggi.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma apa yang menghasilkan performa terbaik dalam penelitian ini?",{"text":85,"@type":77},"Random Forest menghasilkan akurasi tertinggi pada blind testing dengan Accuracy 0.8678 dan F1-score 0.8756 (89,2%).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]