[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128551-en":3,"doc-seo-128551-105":30,"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":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},128551,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Quantifying Uncertainty in Production Forecasting Using Machine Learning - A Thesis","Estimating reserves depends on forecasting hydrocarbon production, which contains uncertainty and bias. This thesis compares deterministic decline curve approaches with probabilistic methods and evaluates whether a multivariate machine learning approach can produce probabilistically reliable forecasts. A Gradient Boosting Regressor with quantiles generates cumulative production predictions for future months. Performance is assessed via RMSE, calibration-based probabilistic reliability, predicted uncertainty windows, and computational cost. Results show improved accuracy, better calibration, reduced uncertainty, and lower runtime versus an existing PDCA method for 438 Midland Basin wells.","QUANTIFYING UNCERTAINTY IN PRODUCTION FORECASTING USING  \nMACHINE LEARNING  \nA Thesis  \nby  \nTHOMAS MATTHEW BUTTON  \nSubmitted to the Graduate and Professional School of  \nTexas A&M University  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nChair of Committee, Committee Members,  \nHead of Department,  \nDuane McVay W. John Lee Siddharth Misra Shuang Zhang Akhil Datta-Gupta  \nDecember 2022  \nMajor Subject: Petroleum Engineering  \nCopyright 2022 Thomas Button  \nABSTRACT  \nEstimating reserves—economically recoverable volumes of hydrocarbons in a company’s portfolio—requires forecasting hydrocarbon production, which is prone to significant uncertainty and bias. Accurately quantifying this uncertainty is paramount to estimators understanding risk and projects meeting expectations.  \nTypically, production forecasts are made deterministically using Decline Curve Analysis (DCA) . However, production forecasts can also be created probabilistically using Probabilistic Decline Curve Analysis (PDCA) . In recent years, some reserves evaluators have turned to multivariate Machine Learning (ML) models to perform deterministic production forecasts, due to ML models’ ability to handle large datasets and include properties other than production in the forecast. However, these models are deterministic and, to the best of my knowledge, there has been no standalone probabilistic adaptation published in the petroleum literature as of yet.  \nThe aims ofthis research were to determine if a ML method was probabilistically reliable in forecasting production and to determine if the accuracy, probabilistic reliability, predicted uncertainty, and computational cost of this method was superior to an existing PDCA method.  \nA Gradient Boosting Regressor (GBR) was adapted to generate cumulative production predictions by training three separate models for each of the 10%, 50% and 90% quantiles. Predictions were made with this Gradient Boosting Regressor with Quantiles (GBRQ) method for future months based on the first 12 months of cumulative production history for the training wells, the target cumulative production at the forecasted month for the training wells, and the first 12 months of cumulative production history for the test wells.  \nPrediction accuracy was measured using the root mean square error (RMSE) between the predicted median (P50) and true values as well as between the predicted mean and true values. Probabilistic reliability was assessed using calibration plots in which the frequency with which actual production values were less than predicted production values at each quantile was plotted against the assigned probability. Predicted uncertainty was assessed using an average normalized uncertainty window and cost was compared on the basis of computational time.  \nThe GBRQ method was more accurate at late times, was more probabilistically reliable, predicted less uncertainty, and was less computationally intensive than a published Probabilistic Decline-Curve-Analysis (PDCA) method for a dataset consisting of 438 conventional wells in the Midland Basin.  \nThe GBRQ methodology can be useful to three groups: (1) reserves estimators, who can make point estimates and full forecasts of probabilistic production comparatively fast and with probabilistic reliability for large datasets; (2) reserves auditors, who can quickly use this method to compare with an auditee’s probabilistic production forecast; and (3) investors and banks, who can evaluate asset acquisitions and divestitures with well-calibrated probabilistic production  \nforecasts.  \nDEDICATION  \nTo my mother, Rene, and my father, Alvin who have supported throughout my graduate studies and career development.  \nACKNOWLEDGEMENTS  \nI would like to thank Dr. Duane A. McVay, my committee chair for his fastidious guidance and support during my thesis research. I would also like to thank Dr. John Lee, Dr. Siddharth Misra, and Dr. Shuang Zhang for serving on my advi","cbCaia2HzVh8PCwm","https://ap.wps.com/l/cbCaia2HzVh8PCwm","pdf",2467273,1,91,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgements\n# Contributors and Funding Sources\n# List of Figures\n# List of Tables\n# Introduction","[{\"question\":\"Why is quantifying uncertainty important in production forecasting for reserves estimation?\",\"answer\":\"Reserves estimation relies on hydrocarbon production forecasts that carry significant uncertainty and bias. Quantifying uncertainty helps estimators understand risk and helps projects meet expectations.\"},{\"question\":\"How does the proposed machine learning method generate probabilistic forecasts?\",\"answer\":\"A Gradient Boosting Regressor with quantiles trains separate models to predict 10%, 50%, and 90% quantiles. It then produces cumulative production predictions for future months using early production history for training and test wells.\"},{\"question\":\"How were probabilistic reliability and uncertainty evaluated in the study?\",\"answer\":\"Probabilistic reliability was assessed using calibration plots comparing assigned probabilities to the observed frequency of actual values falling below predicted quantiles. Predicted uncertainty was evaluated with an average normalized uncertainty window, and computational cost was compared using computational time.\"}]","Quantifying Uncertainty in Production Forecasting Using Machine Learning - A Thesis | PDF",1786001694,229,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"quantifying-uncertainty-in-production-forecasting-using-machine-learning-a-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/quantifying-uncertainty-in-production-forecasting-using-machine-learning-a-thesis/128551/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","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},"Why is quantifying uncertainty important in production forecasting for reserves estimation?","Question",{"text":76,"@type":77},"Reserves estimation relies on hydrocarbon production forecasts that carry significant uncertainty and bias. Quantifying uncertainty helps estimators understand risk and helps projects meet expectations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed machine learning method generate probabilistic forecasts?",{"text":81,"@type":77},"A Gradient Boosting Regressor with quantiles trains separate models to predict 10%, 50%, and 90% quantiles. It then produces cumulative production predictions for future months using early production history for training and test wells.",{"name":83,"@type":74,"acceptedAnswer":84},"How were probabilistic reliability and uncertainty evaluated in the study?",{"text":85,"@type":77},"Probabilistic reliability was assessed using calibration plots comparing assigned probabilities to the observed frequency of actual values falling below predicted quantiles. Predicted uncertainty was evaluated with an average normalized uncertainty window, and computational cost was compared using computational time.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]