{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/132849"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/132849","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A digital approach to the management of brownfields","abstract":"This thesis investigates analytic and data-mining methods that can be used for the management of petroleum brownfields, specifically as it applies to the surveillance, analysis, & optimization of gas lifted oil wells. Building on the output of validated physics-based models, this thesis investigates a range of analytic methods which may be used to determine a probable depth of gas lift injection of wells without pressure gauges, and finds that the Random Forest method coupled with a k-means clustering algorithm can offer good results. Additionally, this thesis shows how a pan matrix profile may be used to efficiently identify patterns (motifs) in the real time pressure signatures of wells. Understanding of the motifs are assessed through a physics-based model, providing a useful tool for engineers to perform surveillance of large well count areas, which are typical for brownfields.","abstract_html":"This thesis investigates analytic and data-mining methods that can be used for the management of petroleum brownfields, specifically as it applies to the surveillance, analysis, & optimization of gas lifted oil wells. Building on the output of validated physics-based models, this thesis investigates a range of analytic methods which may be used to determine a probable depth of gas lift injection of wells without pressure gauges, and finds that the Random Forest method coupled with a k-means clustering algorithm can offer good results. Additionally, this thesis shows how a pan matrix profile may be used to efficiently identify patterns (motifs) in the real time pressure signatures of wells. Understanding of the motifs are assessed through a physics-based model, providing a useful tool for engineers to perform surveillance of large well count areas, which are typical for brownfields.","abstract_has_math":false,"creators":["Partington, Ben (Benjamin Francis)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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