[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128537-en":3,"doc-seo-128537-105":31,"detail-sidebar-cat-0-en-105":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},128537,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Artificial Lift Selection Methods in Conventional and Unconventional Wells - A Summary and Review from Old Techniques to Machine Learning Applications","Artificial lift (AL) selection is a critical process for improving oil and gas production from reservoirs. The article reviews the evolution of AL selection in conventional and unconventional wells, outlining key challenges and the influence of production, reservoir, and fluid data. It considers economic and environmental constraints alongside operational conditions. The review also evaluates machine learning (ML) approaches for AL selection, focusing on improved selection accuracy and reduced data analysis time, supporting researchers and practitioners.","Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning  \napplications .  \nMAHDI, M.A.A., AMISH, M. and OLUYEMI, G.  \n2024  \nThis document was downloaded from [https://openair.rgu.ac.uk](https://openair.rgu.ac.uk)  \nArtificial Lift Selection Methods in Conventional and Unconventional Wells: A Summary and Review from Old Techniques to Machine Learning Applications  \nMohaned Alhaj A. Mahdi 1,2 ; M. Amish 1, G. Oluyemi 1  \n¹School of Engineering, Garthdee Road, Robert Gordon University, Aberdeen, AB10 7GJ, UK.  \n²SLB  \nAbstract:- Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods.  \nKeywords:- Artificial Lift, Selection, Conventionals, Unconventionals, Machine Learning.  \nI. INTRODUCTION  \nThe selection of the optimum Artificial Lift (AL) to achieve the highest recovery is a real challenge in the petroleum industry. Optimal selection is a requirement for obtaining the maximum profit from an oil well (Bucaram and Patterson 1994) . Several factors determine the selection process: depth, rates, reservoir and fluid properties, initial and operating cost, and geographical and environmental aspects. Special AL selection techniques are required to cope with different reservoir, well, and field conditions, for instance: high-viscosity oil, high water cut, sand, gas, low reservoir pressures, high temperatures, low-productivity wells, surface facilities, as well as human interference. Historically, the AL selection process generally begins by studying the advantagesand disadvantages of each method. Then the elimination depends on the engineers’ decision based on their analysis of the AL record, field data availability, and failure history. Since these factors change over time, the AL design for current production conditions without considering future production results in high inconstancy rates and fluctuations in lifting selection (JPT staff 2014; Lea and Nickens 1999). The following sections explore old and recent selection criteria in conventional and unconventional wells (conventionals and unconventionals) from the literature and the various techniques used by the engineers and factors considered.  \nII. AL SELECTION IN CONVENTIONALS  \nAt the early 1980s, Neely et al. (1981) summarised the criteria for selecting four methods of lifting, namely gas lift (GL), sucker rod pump (SRP), electrical submersible pump (ESP), and hydraulic pump (HP), by examining their advantages and disadvantages in relation to reservoir and well properties. They found that SRPs are suitable for low volumes but not recommended for offshore or residential areas, or wells prone to sand production. Continuous gas lift (CGL) is suitable for high volumes, high bottom hole pressure (BHP), and handling solids and sand; however, it is limited by back pressure and high costs. Intermittent gas lift (IGL) is less expensive than continuous gas lift (CGL) but yields lower volumes. ESP is suitable for high volumes and confined spaces, such as offshore platforms, and can tolerate deviations of up to 80°. However, major drawbacks of ESP include sand production, workover costs, and inefficiency at rates below 150 B/D. HPs (piston pump (HPP) and jet pump (HJP)) are suitable for deep we","cbCaituj9tUassXx","https://ap.wps.com/l/cbCaituj9tUassXx","pdf",707090,2,1,16,"English","en",105,"# Introduction\n# AL Selection in Conventionals","[{\"question\":\"What makes artificial lift (AL) selection challenging in the petroleum industry?\",\"answer\":\"Selecting the optimum AL method is challenging because many interacting factors determine performance and profit, including well depth, flow rates, reservoir/fluid properties, costs, and operating and environmental conditions.\"},{\"question\":\"How were early AL selection criteria developed for conventional wells?\",\"answer\":\"Early work summarized and compared major lifting methods (gas lift, sucker rod pumps, ESP, hydraulic pump) using advantages and disadvantages tied to reservoir and well properties, leading to structured decision criteria such as selection trees, charts, and reference tables.\"},{\"question\":\"What role do machine learning (ML) techniques play in AL selection today?\",\"answer\":\"The article highlights ML as a way to improve the accuracy of AL method selection and to reduce the time required for data analysis during the selection process.\"}]","Artificial Lift Selection Methods in Conventional and Unconventional Wells - A Summary and Review from Old Techniques to Machine Learning Applications | PDF",1786001609,40,{"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},"artificial-lift-selection-methods-in-conventional-and-unconventional-wells-a-summary-and-review-from-old-techniques-to-machine-learning-applications","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/artificial-lift-selection-methods-in-conventional-and-unconventional-wells-a-summary-and-review-from-old-techniques-to-machine-learning-applications/128537/",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","2026-08-24","2026-08-06",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},"What makes artificial lift (AL) selection challenging in the petroleum industry?","Question",{"text":76,"@type":77},"Selecting the optimum AL method is challenging because many interacting factors determine performance and profit, including well depth, flow rates, reservoir/fluid properties, costs, and operating and environmental conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were early AL selection criteria developed for conventional wells?",{"text":81,"@type":77},"Early work summarized and compared major lifting methods (gas lift, sucker rod pumps, ESP, hydraulic pump) using advantages and disadvantages tied to reservoir and well properties, leading to structured decision criteria such as selection trees, charts, and reference tables.",{"name":83,"@type":74,"acceptedAnswer":84},"What role do machine learning (ML) techniques play in AL selection today?",{"text":85,"@type":77},"The article highlights ML as a way to improve the accuracy of AL method selection and to reduce the time required for data analysis during the selection process.","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},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]