[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126192-en":3,"doc-seo-126192-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126192,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of supervised machine learning classification models to identify borehole breakouts in carbonate reservoirs based on conventional log data","Breakouts provide crucial information for evaluating in situ stresses and validating geomechanical models. Conventional image logs and multiple pad calipers can identify breakout-related borehole deformation, but they are often unavailable in many wells due to cost and technical constraints. This study develops supervised classification using KNN, Decision Tree, and Random Forest with conventional wireline log subintervals as inputs. The Random Forest model delivers the highest reliability, achieving 92.41% accuracy and strong precision, recall, and F1 performance, demonstrating fast and practical breakout identification from standard logs.","Application of supervised machine learning classification models to identify borehole breakouts in carbonate reservoirs based on conventional log data  \nNazir MAFAKHERI BASHMAGH*, Weiren LIN ** & Kazuya ISHITSUKA **  \n* Earth and Resource System, Graduate School of Engineering, Kyoto University, Room 118, C1, Katsura Campus, Kyoto, Japan  \n**Member of ISRM: Earth and Resource System, Graduate School of Engineering, Kyoto University, C1-108&109, Katsura Campus, Kyoto, Japan  \nReceived 19 11 2023; received in revised form 17 02 2024; accepted 07 03 2024  \nABSTRACT  \nBreakouts provide crucial information regarding evaluating in situ stresses and verifying the geomechanical model. One of the most common methods for identifying borehole deformation, such as breakouts and drilling-induced fractures, is a combination of image logs and multiple pad calipers to identify borehole elongation. However, image logs are suitable for geomechanical studies but are usually unavailable in most drilled wells due to technical and financial reasons. Conventional wireline logs, including gammaray, neutron porosity, density, resistivity, and single pad caliper, on the other hand, are widely used in drilled wells. Moreover, in recent years, machine learning has been widely applied to classification and optimization problems in the geology and petroleum industry. This research investigated the possibility of predicting the occurrence of borehole breakouts. The models employed encompass K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) . The input values used in this study are 4099 subintervals from conventional logs and their corresponding identified breakouts from real data as classification labeling parameters for the same interval. The performance capacity of the classification models was evaluated based on Accuracy, Precision, Recall, and F1 Score. The results are promising, and RF classification performance is more reliable than the other models. Furthermore, the RF predictive model had Accuracy, Precision, Recall, and F1 Scores equal to 92.41%, 86.36%, 87.08%, and 86.7%, respectively, superseding the performance of the other models. Overall, this study shows that the application of machine learning classification models demonstrated a reasonable and fast performance in identifying borehole breakouts from conventional logs.  \nKeywords: Breakout, Machine learning, classification, K-nearest neighbors, Decision tree, Random Forest  \n1. INTRODUCTION  \nCarbonate reservoirs are considered typical conventional oil and gas reservoirs, characterized by their unique porosity and permeability systems along with Sandstone reservoirs. Economic oil and gas production from Carbonate reservoirs is a big challenge for the industry. Due to the relatively low permeability of Carbonates, generally, production would require hydraulic fracturing, horizontal drilling, and multiple sidetracks (Lee and Ong, 2018; Manshad et al., 2019) . In addition, wellbore stability plays a significant role in petroleum engineering and geotechnical engineering, influencing the success and safety of drilling operations (Dahab et al., 2020; Liu et al.,  \n2016). Wellbore instability issues, such as borehole breakouts, can significantly compromise wellbore stability.  \nBorehole breakouts, a form of wellbore instability, are local enlargements of the borehole diameter usually associated with in-situ stress and formation strength. Breakouts occur when the circumference stress exceeds the strength of the rock surrounding the borehole, causing rock failure (Zoback et al., 1985) . They create an unstable wellbore condition that, in the worst-case scenario, can result in a complete wellbore collapse if not promptly addressed (Moore et al., 2012; Neeamy and Selman, 2020) . Borehole breakouts impact drilling operations in numerous ways. First, they can lead to excessive non-productive time due to stuck pipe incidents, wellbore collapse, orthe need for remedial operations (Cheath","cbCaivaCUA0aGRkD","https://ap.wps.com/l/cbCaivaCUA0aGRkD","pdf",1878455,10,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background and importance of borehole breakouts\n## Effects on drilling operations and completion design","[{\"question\":\"Why are borehole breakouts important in carbonate reservoir drilling?\",\"answer\":\"Breakouts relate to in situ stress and rock strength, and they can compromise wellbore stability, potentially leading to collapse if not addressed. They also affect drilling efficiency, sand production, and completion design decisions.\"},{\"question\":\"What input data and models are used to classify borehole breakouts?\",\"answer\":\"The study uses conventional wireline log subintervals (4099 subintervals) with identified breakouts as classification labels. Models evaluated include K-Nearest Neighbors, Decision Tree, and Random Forest.\"},{\"question\":\"Which classification model performs best and how is performance measured?\",\"answer\":\"Random Forest shows the most reliable performance among the tested models. Model quality is assessed using Accuracy, Precision, Recall, and F1 Score, with reported Random Forest values of 92.41% accuracy and strong metric results across precision, recall, and F1.\"}]","Application of supervised machine learning classification models to identify borehole breakouts in carbonate reservoirs based on conventional log data | PDF",1785903719,30,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"application-of-supervised-machine-learning-classification-models-to-identify-borehole-breakouts-in-carbonate-reservoirs-based-on-conventional-log-data","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/application-of-supervised-machine-learning-classification-models-to-identify-borehole-breakouts-in-carbonate-reservoirs-based-on-conventional-log-data/126192/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are borehole breakouts important in carbonate reservoir drilling?","Question",{"text":77,"@type":78},"Breakouts relate to in situ stress and rock strength, and they can compromise wellbore stability, potentially leading to collapse if not addressed. They also affect drilling efficiency, sand production, and completion design decisions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What input data and models are used to classify borehole breakouts?",{"text":82,"@type":78},"The study uses conventional wireline log subintervals (4099 subintervals) with identified breakouts as classification labels. Models evaluated include K-Nearest Neighbors, Decision Tree, and Random Forest.",{"name":84,"@type":75,"acceptedAnswer":85},"Which classification model performs best and how is performance measured?",{"text":86,"@type":78},"Random Forest shows the most reliable performance among the tested models. Model quality is assessed using Accuracy, Precision, Recall, and F1 Score, with reported Random Forest values of 92.41% accuracy and strong metric results across precision, recall, and F1.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":123},"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":20,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]