A digital approach to the management of brownfields

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Partington, Ben (Benjamin Francis)

Advisor dc:contributor.advisor

• John Williams.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132849

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132849