[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127845-en":3,"doc-seo-127845-105":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},127845,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","TIME SERIES PRODUCTION FORECASTING AND UNCERTAINTY QUANTIFICATION USING PROBABILISTIC DECLINE CURVE ANALYSIS AND MACHINE LEARNING TECHNIQUES - FOR UNCONVENTIONAL RESERVOIRS","Production forecasting and uncertainty quantification remain difficult tasks, especially when combining decline-curve analysis (DCA) models with probabilistic methods or deep-learning approaches. This thesis applies three probabilistic algorithms—Approximate Bayesian Computation, conventional bootstrap, and modified bootstrap—integrated with deterministic DCA models (Arps, Duong, and logarithmic growth analysis). It also employs LSTM and Transformer neural networks, with LSTM hyperparameters tuned using Bayesian optimization. Experiments on 400 Marcellus shale gas wells compare P10/P50/P90 uncertainty bands and MAPE across multiple hind-cast lengths.","TIME SERIES PRODUCTION FORECASTING AND UNCERTAINTY QUANTIFICATION USINGPROBABILISTIC DECLINE CURVE ANALYSIS AND MACHINE LEARNING TECHNIQUES FORUNCONVENTIONAL RESERVOIRS  \nbyKapil Hari Sachdev  \nB.Tech,Dr.Vishwanath Karad MIT World Peace University,2021  \nA Thesis submitted in Partial Fulfillment of the Requirementsfor the Degree of  \nMaster of Science  \ninPetroleum Engineering  \nUniversity of Alaska FairbanksAugust 2024  \nAPPROVED:  \nObadare Awoleke,Committee ChairArghya Das,Committee Co-ChairScott Goddard,Committee MemberAbhijit Dandekar,Chair  \nDepartment of Petroleum Engineering  \nWilliam Schnabel,Dean  \nCollege of Engineering and Mines  \nRichard Collins,DirectorGraduate School  \nC Copyright by Kapil SachdevAll Rights Reserved  \nDedication  \nI want to dedicate this thesis work to the Supreme Personality of Godhead,Lord Shri Krishna,for his mercy and strength.  \nAbstract  \nResearchers have long been engaged in the challenging task of production forecasting anduncertainty quantification,often by combining Decline Curve Analysis(DCA)models withprobabilistic algorithms.Also,deep-learning approaches have been explored for productionforecasting.In this work,we present the application of three probabilistic algorithms combinedwith various deterministic DCA models.We have also applied two machine learning(deeplearning)algorithms for production forecasting and uncertainty quantification.Finally,we havecompared the results of ML algorithms with the probabilistic algorithms based on the obtainedMAPE values to conclude whether probabilistic or ML algorithms are better for productionforecasting.  \nOur analysis commences with the utilization of three probabilistic algorithms,namelyApproximate Bayesian Computation (ABC),Conventional Bootstrap(CBM),and ModifiedBootstrap methods(MBM).Each algorithm was integrated with three deterministic DCA models:Arps,Duong,and Logarithmic Growth Analysis (LGA).We then harnessed the power of machinelearning(ML)algorithms called long-short-term memory(LSTM)and Transformer neuralnetworks.The hyperparameters for the LSTM algorithm were chosen using BayesianOptimization.We conducted a comprehensive study on 400 gas wells from the Marcellusunconventional shale basin,evaluating LSTM,Transformer,and each probabilistic-DCAcombination.Our hindcasting,which employed 12,24,36,48,and 60 months of historicalproduction data(hind-casts)to forecast up to 96 months,yielded forecasts that are essentially10th,50th,and 90th percentiles,providing the P10,P50,and P90 estimates,respectively.Theseestimates,which we refer to as uncertainty bands,vividly demonstrate uncertainty quantificationas we progress from 12-60 months of hind-casts.In simple words,as we increase the use ofhistorical data from 12 to 60 months,the P10 and P90 bands tighten,depicting that uncertaintydecreases in the forecasts.Furthermore,we present the Mean Absolute Percentage Error(MAPE)for each probabilistic algorithm-DCA combination and LSTM algorithm for each hind-cast lengthfor comprehensive comparison and conclusive insights.  \nOur findings are helpful and should inspire confidence in the potential of ML algorithms forproduction forecasting and uncertainty quantification.We demonstrate the superiorperformance of both MLalgorithms (LSTM and Transformers),particularly for 12 to 36 months ofhind-cast for MAPE values and uncertainty bands.The compression of uncertainty bands withincreasing hind-cast lengths indicates a decrease in uncertainty as production history increases;a promising result.Furthermore,the MAPE value decreases as we extend the hind-cast periodfrom 12 to 60 months,suggesting improved accuracy with longer hind-casts.  \nThe uniqueness of this work is in the comparison of the ML algorithms(Transformers and LSTM)with the probabilistic DCA approach as discussed above.Also,the proportionality scaling function(PSF)that is introduced in this work allows the analyst to be able to capture the uncertaintyassociated with machine learning forecasts in a ","cbCainq5vTYZujgP","https://ap.wps.com/l/cbCainq5vTYZujgP","pdf",3611492,4,1,58,"English","en",105,"# Abstract\n# Acknowledgments\n# Table of Contents\n# List of Figure\n# List of Tables\n# Nomenclature\n# Chapter 1: Introduction\n# Chapter 2: Methodology\n## Collection and cleaning of the data and Marcellus shale overview\n## Bayesian Method—approximate Bayesian computation (ABC)\n## Frequentist method—conventional Bootstrap (CBM)\n## Frequentist method—modified Bootstrap (MBM)\n## Machine learning method—Long-short-term memory (LSTM)","[{\"question\":\"What probabilistic algorithms are used for uncertainty quantification in this thesis?\",\"answer\":\"The thesis uses Approximate Bayesian Computation (ABC), Conventional Bootstrap (CBM), and Modified Bootstrap (MBM). Each method is paired with deterministic DCA models to produce probabilistic forecasts and uncertainty bands.\"},{\"question\":\"Which deterministic decline-curve analysis (DCA) models are combined with the probabilistic algorithms?\",\"answer\":\"The probabilistic methods are integrated with Arps, Duong, and Logarithmic Growth Analysis (LGA). This combination supports production forecasting with quantified uncertainty.\"},{\"question\":\"How are uncertainty bands and forecast accuracy evaluated and compared?\",\"answer\":\"Forecasts are produced as P10, P50, and P90 percentiles (P10/P50/P90), forming uncertainty bands across hind-cast lengths. Accuracy is compared using MAPE values for each probabilistic-DCA combination and for the LSTM/Transformer models.\"}]","TIME SERIES PRODUCTION FORECASTING AND UNCERTAINTY QUANTIFICATION USING PROBABILISTIC DECLINE CURVE ANALYSIS AND MACHINE LEARNING TECHNIQUES - FOR UNCONVENTIONAL RESERVOIRS | PDF",1785942303,146,{"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},"time-series-production-forecasting-and-uncertainty-quantification-using-probabilistic-decline-curve-analysis-and-machine-learning-techniques-for-unconventional-reservoirs","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/time-series-production-forecasting-and-uncertainty-quantification-using-probabilistic-decline-curve-analysis-and-machine-learning-techniques-for-unconventional-reservoirs/127845/",{"url":53,"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 probabilistic algorithms are used for uncertainty quantification in this thesis?","Question",{"text":76,"@type":77},"The thesis uses Approximate Bayesian Computation (ABC), Conventional Bootstrap (CBM), and Modified Bootstrap (MBM). Each method is paired with deterministic DCA models to produce probabilistic forecasts and uncertainty bands.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which deterministic decline-curve analysis (DCA) models are combined with the probabilistic algorithms?",{"text":81,"@type":77},"The probabilistic methods are integrated with Arps, Duong, and Logarithmic Growth Analysis (LGA). This combination supports production forecasting with quantified uncertainty.",{"name":83,"@type":74,"acceptedAnswer":84},"How are uncertainty bands and forecast accuracy evaluated and compared?",{"text":85,"@type":77},"Forecasts are produced as P10, P50, and P90 percentiles (P10/P50/P90), forming uncertainty bands across hind-cast lengths. Accuracy is compared using MAPE values for each probabilistic-DCA combination and for the LSTM/Transformer models.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"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":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]