A digital approach to the management of brownfields
Name
1263244922-MIT.pdf
Size
6.81 MB
Format
Adobe PDF
Checksum (MD5)
187bfe636db99d0e14b59b950c1664cc
Author(s)
Partington, Ben
(Benjamin Francis)
Advisor(s)
John Williams.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020
Cataloged from the official version of thesis. "September 2020."
Includes bibliographical references (pages 121-129).
Subjects
Engineering and Management Program.
System Design and Management Program.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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