Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling
Name
1263351398-MIT.pdf
Size
10.34 MB
Format
Adobe PDF
Checksum (MD5)
f3ee4677a5caf8c7156c8648a5856410
Author(s)
Wilson, Oliver John.
Advisor(s)
Alexandre Jacquillat.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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.
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 115-118).
Subjects
Engineering and Management Program.
System Design and Management Program.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
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