[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126565-en":3,"doc-seo-126565-105":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},126565,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","An Artificial Lift Selection Approach Using Machine Learning - A Case Study in Sudan","Machine learning is applied to artificial lift (AL) selection using field production datasets from a Sudanese oil field. Five supervised ML algorithms are trained to build a selection model, achieving up to 93% accuracy and indicating that predicted AL options can outperform the actual field selections. The study identifies key production parameters for AL type and size selection and ranks six critical factors—gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR. A universal model is proposed to maximize oil production and profitability while reducing analysis time and selection inconsistency.","An artificial lift selection approach using machine learning: a case study in Sudan.  \nMAHDI, M.A.A., AMISH, M. and OLUYEMI, G.  \n2023  \nThis document was downloaded from [https://openair.rgu.ac.uk](https://openair.rgu.ac.uk)  \nenergies   \nArticle  \nAn Artificial Lift Selection Approach Using Machine Learning: A Case Study in Sudan  \nMohaned Alhaj A. Mahdi *, Mohamed Amish and Gbenga Oluyemi  \nCitation: Mahdi, M.A.A.; Amish, M.; Oluyemi, G. An Artificial Lift Selection Approach Using Machine Learning: A Case Study in Sudan.  \nEnergies 2023, 16, 2853. [https://](https://)[ ](https://)[doi.org/10.3390/en16062853](doi.org/10.3390/en16062853)  \nAcademic Editors: Mohamed Mahmoud and Zeeshan Tariq  \nReceived: 14 February 2023  \nRevised: 28 February 2023  \nAccepted: 16 March 2023  \nPublished: 19 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSchool of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK  \n* [Correspondence: m.mahdi1@rgu.ac.uk](Correspondence: m.mahdi1@rgu.ac.uk)  \nAbstract: This article presents a machine learning (ML) application to examine artificial lift (AL) selection, using only field production datasets from a Sudanese oil field. Five ML algorithms were used to develop a selection model, and the results demonstrated the ML capabilities in the optimum selection, with accuracy reaching 93% . Moreover, the predicted AL has a better production performance than the actual ones in the field. The research shows the significant production parameters to consider in AL type and size selection. The top six critical factors affecting AL selection are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR. This article contributes significantly to the literature and proposes a new and efficient approach to selecting the optimum AL to maximize oil production and profitability, reducing the analysis time and production losses associated with inconsistency in selection and frequent AL replacement. This study offers a universal model that can be applied to any oil field with different parameters and lifting methods.  \nKeywords: artificial lift; machine learning; production data; supervised learning; algorithms  \n1. Introduction  \nSome wells can naturally produce oil at the start of production by using reservoir primary drive mechanisms such as solution gas drive, gas expansion, and strong water drive. However, most reservoir energies are finite, will deplete over time, and cannot naturally lift hydrocarbons to the surface [1] . An AL is a production system unit that lifts the hydrocarbons from the reservoir to the surface to support insufficient reservoir energy [2] .  \nThe AL is a milestone in the oil and gas industry since it accounts for 95% of worldwide oil production [3] . There are several types of AL: sucker rod pumping (SRP) or beam pumping unit (BPU), progressive cavity pump (PCP), gas lift (GL), electrical submersible pump (ESP), plunger lift (PL), hydraulic jet pump (HJP), and hydraulic piston pump (HPP) . SRP produces approximately 70% of oil worldwide and is postulated to be the oldest lifting method [4] . Optimum AL selection is critical since it determines the daily fluid production (daily revenue) that the oil corporations will gain. The AL selection techniques in the literature study the advantages and disadvantages of each lifting method considering field conditions, well, and reservoir parameters [5,6] . They use qualitative methods, primarily relying on engineers' personal experience [7] . The critical issue is that the field parameters are dependent on and change over production years. In addition, the parameters are neither theoretically n","cbCaitU38SdCfqkV","https://ap.wps.com/l/cbCaitU38SdCfqkV","pdf",2483306,2,1,16,"English","en",105,"# Introduction\n## Artificial lift and selection challenges\n## Research aims and proposed machine-learning model\n# Materials Preparation","[{\"question\":\"What machine learning method is used for artificial lift selection in the study?\",\"answer\":\"The study uses supervised machine learning with Python, training algorithms on field production inputs and outputs to learn relevant selection patterns.\"},{\"question\":\"How accurate is the proposed selection model?\",\"answer\":\"The results show accuracy up to 93% for the optimum AL selection derived from the model.\"},{\"question\":\"Which factors are identified as the most critical for AL selection?\",\"answer\":\"The top six factors are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR.\"}]","An Artificial Lift Selection Approach Using Machine Learning - A Case Study in Sudan | PDF",1785933360,40,{"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},"an-artificial-lift-selection-approach-using-machine-learning-a-case-study-in-sudan","",{"@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/an-artificial-lift-selection-approach-using-machine-learning-a-case-study-in-sudan/126565/",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","2026-08-25","2026-08-05",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 machine learning method is used for artificial lift selection in the study?","Question",{"text":76,"@type":77},"The study uses supervised machine learning with Python, training algorithms on field production inputs and outputs to learn relevant selection patterns.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How accurate is the proposed selection model?",{"text":81,"@type":77},"The results show accuracy up to 93% for the optimum AL selection derived from the model.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors are identified as the most critical for AL selection?",{"text":85,"@type":77},"The top six factors are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR.","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"]