2026-09-19T04:39:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1328752025-10-30T15:50:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Alexandre Jacquillat.
Wilson, Oliver John.
Massachusetts Institute of Technology. Engineering and Management Program.
System Design and Management Program.
Massachusetts Institute of Technology. Engineering and Management Program
2021-10-08T17:10:17Z
2021-10-08T17:10:17Z
2020
https://hdl.handle.net/1721.1/132875
1263351398
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 115-118).
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.
by Oliver John Wilson.
S.M. in Engineering and Management
S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program
118 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Engineering and Management Program.
System Design and Management Program.
Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling
Thesis
application/pdf
2021-10-08T17:10:17Z
Publication
Master
SysDes
unknown
unknown
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<Abstract>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.</Abstract>
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