[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118850-en":3,"doc-seo-118850-105":30,"detail-sidebar-cat-0-en-105":90},{"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},118850,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning Algorithms for Predicting Liquid Loading in Gas Wells","Liquid loading describes when the gas produced from a well cannot transport the co-produced liquid, causing accumulation in the wellbore. This buildup reduces gas production and can even halt production under severe conditions. Liquid loading in gas wells emerges when critical gas velocity drops below a threshold, lowering gas flow rate and production. Experiments in a multiphase flow loop were performed to study vertical liquid buildup and train machine learning models to predict loaded versus unloaded states.","University of North Dakota  \nUND Scholarly Commons  \n\n| Petroleum Engineering Posters and Presentations | Department of Petroleum Engineering |\n| --- | --- |\n\n6-20-2023  \nMachine Learning Algorithms for Predicting Liquid Loading in Gas Wells  \nNassim Bouabdallah  \n[nassim.bouabdallah@und.edu](nassim.bouabdallah@und.edu)[ ](nassim.bouabdallah@und.edu)Abdeldjalil Latrach  \nAimene Aihar  \nAdesina Fadairo  \nUniversity of North Dakota, [adesina.fadairo@ndus.edu](adesina.fadairo@ndus.edu)  \nHow does access to this work benefit you? Let us know!  \nFollow this and additional works at: [https://commons.und.edu/pe-pp](https://commons.und.edu/pe-pp)  \nRecommended Citation  \nBouabdallah, Nassim; Latrach, Abdeldjalil; Aihar, Aimene; and Fadairo, Adesina, \"Machine Learning Algorithms for Predicting Liquid Loading in Gas Wells\" (2023) . Petroleum Engineering Posters and Presentations. 3.  \n[https://commons.und.edu/pe-pp/3](https://commons.und.edu/pe-pp/3)  \nThis Poster is brought to you for free and open access by the Department of Petroleum Engineering at UND Scholarly Commons. It has been accepted for inclusion in Petroleum Engineering Posters and Presentations by an authorized administrator of UND Scholarly Commons. For more information, please contact [und.commons@library.und.edu](und.commons@library.und.edu).  \nMachine Learning Algorithms for Predicting Liquid Loading in Gas Wells  \nNassim Bouabdallah1 , Abdeldjalil Latrach 2 , Aimene Aihar 1 , Adesina Fadairo 1  \n1 University of North Dakota, Department of Petroleum Engineering  \n2 University of Wyoming, Department of Energy and Petroleum Engineering  \nAbstract  \nLiquid loading is a term used to describe the situation where the gas produced from a well is unable to carry the liquid that is also produced along with it (Khetib et al. , 2022) . As a result, the liquid starts to accumulate in the wellbore. This accumulation of liquid can cause a decrease in gas production and in severe cases, it may even lead to a complete stoppage of production (Khetib et al. , 2022 , 2023) . The phenomenon of liquid loading in gas wells occurs when the critical gas velocity is less than a certain value, leading to a decrease in gas flow rate and ultimately a decrease in production (Merzoug et al. , 2022) . To simulate this phenomenon and study it in detail, we conducted experiments in a multiphase flow loop. We aimed to compare the results of the experiment with machine learning algorithms to predict the loading and unloading of the well. The purpose of this experiment was to examine the start of liquid accumulation in a gas well using a 2 .4 meter vertical rigid pipe system with a 0 .0508 meter (2 inch) internal diameter (Khetib. Y, 2022) . The study analyzed the flow of gas and liquid in a vertical direction to gain insight into how liquid builds up ina vertical tube as gas flow decreases. We varied the gas and liquid flow rates to simulate different conditions and recorded the pressure and flow rate data (Khetib. Y, 2022) . We used this data to train machine learning algorithms such as Support Vector Machines (SVMs) (Ifrene et al. , 2023), Random Forests, XGBoost, and Neural Networks, to predict whether the well is loaded or unloaded. We then compared the predictions of the machine learning algorithms with the experimental data.  \nObjectives  \nThe purposes of this study are to develop a reliable predictive model using machine learning algorithms for accurately determining liquid loading in gas wells, contributing to more efficient operations and enhanced safety measures.  \nTo investigate and compare the predictive performance of different machine learning algorithms, including (but not limited to) decision trees, random forest and support vector machines for the given problem. To design a system that can utilize readily available operational data from gas wells, reducing the need for additional instrumentation or expensive data gathering processes.  \nFig 1. Photo of the experimental facility ( Khetib.","cbCaibcVlGKNb0xz","https://ap.wps.com/l/cbCaibcVlGKNb0xz","pdf",757014,1,2,"English","en",105,"# Abstract\n# Objectives\n# Methods\n## Data collection and cleaning\n## Correlation analysis\n# Results\n# Conclusion","[{\"question\":\"What causes liquid loading in gas wells?\",\"answer\":\"Liquid loading occurs when the critical gas velocity becomes lower than a threshold value, decreasing gas flow rate so the gas can no longer carry the produced liquid effectively.\"},{\"question\":\"How was the data collected for model training?\",\"answer\":\"Experiments were run in a multiphase flow loop under varying gas and water flow-rate conditions, recording pressure and flow-rate data. The dataset contained over 10,000 rows and used multiple input variables.\"},{\"question\":\"Which machine learning model performed best?\",\"answer\":\"The Random Forest regressor achieved the highest R-2 score on the test set, producing near-perfect predictions compared with the other evaluated models.\"}]","Machine Learning Algorithms for Predicting Liquid Loading in Gas Wells | PDF",1785720616,5,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"machine-learning-algorithms-for-predicting-liquid-loading-in-gas-wells","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/machine-learning-algorithms-for-predicting-liquid-loading-in-gas-wells/118850/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What causes liquid loading in gas wells?","Question",{"text":74,"@type":75},"Liquid loading occurs when the critical gas velocity becomes lower than a threshold value, decreasing gas flow rate so the gas can no longer carry the produced liquid effectively.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How was the data collected for model training?",{"text":79,"@type":75},"Experiments were run in a multiphase flow loop under varying gas and water flow-rate conditions, recording pressure and flow-rate data. 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