Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling

This thesis describes a stacked ensemble, supervised machine learning problem for well rate estimations utilizing well test features that are far from independent and identically distributed (IID), and exhibit missing data with a not missing at random (MNAR) classification from three different oil fields. This research introduces a novel integrated imputation procedure that combines the imputation model selection with the cross-validation procedure for downstream model tuning without data "leakage"--the primary objective shifts from minimizing the imputation data error to minimizing the downstream hold-out error. A stratified time-slicing rolling forecast cross-validation procedure is implemented to minimize over-fitting from the plethora of statistical assumptions that are violated. This thesis seeks to test a framework that will enable well rate estimations for fields available well test data to improve well surveillance capabilities in order to maximize production metrics and minimize adverse health and environmental impacts.

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

• Wilson, Oliver John.

Advisor dc:contributor.advisor

• Alexandre Jacquillat.

• 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/1721.1/132875

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132875