[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121386-en":3,"doc-seo-121386-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121386,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Evaluation of Machine Learning Algorithms for Flow Rate Estimation in Oil and Gas Industry - Master’s Thesis 2024","Accurate flow-rate measurement for multiphase oil, gas, and water is critical for safe operations and effective optimization in the petroleum industry. With growing availability of process data, machine learning models are used to estimate key parameters and improve representation of production dynamics. This Master’s thesis builds models from simulator data to predict well flow rates. Ten algorithms are evaluated on two oil wells; LSTM achieves the best MAPE, while noise sensitivity and median-filter denoising are analyzed. Prediction uncertainty is quantified via 95% confidence intervals.","[www.usn.no](www.usn.no)  \nFMH606 Master’s Thesis 2024 Industrial IT and Automation  \nEvaluation of Machine Learning Algorithms for Flow Rate Estimation in Oil and Gas  \nIndustry  \nNeville Aloysius D’Souza  \nFaculty of Technology, Natural Sciences and Maritime Sciences  \nCampus Porsgrunn  \n[www.usn.no](www.usn.no)  \nCourse: FMH606 Master’s Thesis 2024  \nTitle: Evaluation of Machine Learning Algorithms for Flow Rate Estimation in Oil and Gas Industry  \nPages: 75  \nKeywords: Machine Learning, Data Validation, Flow Rate Measurements, Errors .  \nStudent: Neville Aloysius D’Souza  \nSupervisor: Gaurav Mirlekar, Carlos Pfeiffer External partner: Equinor  \nSummary:  \nAccurate measurement of flow rate of the multiphase flow of oil, gas and water from the oil wells, is an important part of the oil and gas industry. This enables the safe operation and proper optimization of the production. Therefore much research has been dedicated to improve the accuracy of measurements. Various methods like Virtual flow metering and Multi phase flow meters are used.  \nWith the increasing availability of process data, machine learning algorithms have been applied to create models that are beneficial to the oil and gas industry. They can be used for various parameter estimations, predictive maintenance and so on. The application of these algorithms for flow rate estimation provides a more accurate representation of the oil and gas production process.  \nThe goal of this thesis is to use the simulator data, to create machine learning models. These models are used to predict the flow rates of oil, gas and water from the wells. Two oil wells are evaluated here. Ten machine learning algorithms are evaluated. LSTM provides the best results with MAPE of 1 .96% for Well 1 and 1 .56% for Well 2 . In addition, the effects of noise on the models are explored. Median filter with window size of three provides good noise reduction. Finally the uncertainty of the prediction are quantified using 95% confidence intervals in XGBoost models .  \nThe University of South-Eastern Norway accepts no responsibility for the results and  \nconclusions presented in this report.  \nPreface  \nThis thesis is submitted for the degree of Master of Science in Industrial IT & Automation at University of South-Eastern Norway, Porsgrunn.  \nI am deeply grateful to my thesis supervisors, Gaurav Mirlekar and Carlos Pfeiffer for their support, guidance and supervision of this thesis work.  \nI also want to thank Roshan Sharma for providing the Oil/Gas production model simulator.  \nPorsgrunn, 15th May 2024 Neville Aloysius D’Souza  \nContents  \nPreface 3  \nContents 6  \nList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \nList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9  \n1 Introduction 12  \n1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  \n1.3 System Sketch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  \n1.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  \n1.5 Report Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14  \n2 Literature Review 15  \n2.1 Oil and gas production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15  \n2.1.1 Multiphase Flow metering . . . . . . . . . . . . . . . . . . . . . . . . 15  \n2.2 Virtual Flow Metering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17  \n2.2.1 Data driven VFM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18  \n2.2.1.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . 18  \n2.2.1.2 Data pre processing . . . . . . . . . . . . . . . . . . . . . . 19  \n2.2.1.3 Model development . . . . . . . . . . . . . . . . . . . . . 19  \n2.2.1.4 Prediction of flow rates . . . . . . . . . . . . . . . . . . . . 20  \n2.2.1.5","cbCaijoFyaf74fX0","https://ap.wps.com/l/cbCaijoFyaf74fX0","pdf",2617835,1,75,"English","en",105,"# Introduction\n## Background\n## Objective\n## System Sketch\n## Limitations\n## Report Structure\n# Literature Review\n## Oil and gas production\n## Multiphase Flow metering\n## Virtual Flow Metering\n## Data driven VFM\n## Previous work on machine learning\n# Modelling\n## Description of the oil well\n## Machine learning algorithms\n# Predictions\n## Setup\n## Data splitting\n## Data visualization\n## Well 1\n## Well 2","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To create machine learning models using simulator data that predict flow rates of oil, gas, and water from wells, and evaluate their performance across multiple algorithms.\"},{\"question\":\"Which machine learning algorithm performs best in the evaluation?\",\"answer\":\"LSTM provides the best results, with MAPE of 1.96% for Well 1 and 1.56% for Well 2.\"},{\"question\":\"How does the thesis handle noise and quantify prediction uncertainty?\",\"answer\":\"It explores the effect of noise and finds median filtering with a window size of three helps with noise reduction. It quantifies prediction uncertainty using 95% confidence intervals in XGBoost models.\"}]","Evaluation of Machine Learning Algorithms for Flow Rate Estimation in Oil and Gas Industry - Master’s Thesis 2024 | PDF",1785735417,189,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluation-of-machine-learning-algorithms-for-flow-rate-estimation-in-oil-and-gas-industry-masters-thesis-2024","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-machine-learning-algorithms-for-flow-rate-estimation-in-oil-and-gas-industry-masters-thesis-2024/121386/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To create machine learning models using simulator data that predict flow rates of oil, gas, and water from wells, and evaluate their performance across multiple algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm performs best in the evaluation?",{"text":80,"@type":76},"LSTM provides the best results, with MAPE of 1.96% for Well 1 and 1.56% for Well 2.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis handle noise and quantify prediction uncertainty?",{"text":84,"@type":76},"It explores the effect of noise and finds median filtering with a window size of three helps with noise reduction. 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