Application of Machine Learning in Different Stages of Oil Reservoir Development

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dc.contributor.advisorChen, Zhangxing
dc.contributor.authorWei, Liu
dc.contributor.committeememberRoman, Shor
dc.contributor.committeememberQingye, Lu
dc.contributor.committeememberHaiping, Huang
dc.contributor.committeememberYuntian, Chen
dc.date2023-11
dc.date.accessioned2023-07-07T19:23:33Z
dc.date.available2023-07-07T19:23:33Z
dc.date.issued2023-07
dc.description.abstractGeological and oilfield big data is exponentially expanding. The traditional methods used to identify reservoirs and predict production cannot use historical information and new data effectively. The processes of well logging interpretation and pipeline non-destructive examination (NDE) are time consuming and subjective. Numerical flow simulation models do provide a relatively reliable and appropriate approach to conduct a reservoir analysis, but they are laborious and time consuming. In today’s big data environments, it is increasingly necessary to develop an effective and dependable technique to maximize the benefits of a growing data explosion and extract useful information within all the oilfield data. A machine learning method incorporates various algorithms that provide powerful functions in an oilfield. Massive static and dynamic data is put into training models to identify valuable features and learn nonlinear relationships between different variables and output targets. Advanced models using the benefits of machine learning (ML) will help operators to implement classification and/or prediction tasks. This study compares various ML methods applied to different stages from oil and gas exploration to transportation in oilfields: reservoir identification, prediction of production in new and old wells and non-destructive examination (NDE) of pipelines. These ML methods are proven useful and fast to resolve reservoir classification and production prediction challenges. This work provides a set of systematic ML methods and their respective pertinent predicting parameters providing useful experiences and references for industry and future relative research.
dc.identifier.citationWei, L. (2023). Application of machine learning in different stages of oil reservoir development (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/116713
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/41555
dc.language.isoen
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectReservoir identification
dc.subjectProduction prediction
dc.subjectPipeline safety
dc.subjectMachine learning
dc.subject.classificationEngineering--Petroleum
dc.subject.classificationArtificial Intelligence
dc.titleApplication of Machine Learning in Different Stages of Oil Reservoir Development
dc.typedoctoral thesis
thesis.degree.disciplineEngineering – Chemical & Petroleum
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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