Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/132875 |
| _version_ | 1826217749151481856 |
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| author | Wilson, Oliver John. |
| author2 | Massachusetts Institute of Technology. Engineering and Management Program. |
| author_facet | Massachusetts Institute of Technology. Engineering and Management Program. Wilson, Oliver John. |
| author_sort | Wilson, Oliver John. |
| collection | MIT |
| description | Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020 |
| first_indexed | 2024-09-23T17:08:32Z |
| format | Thesis |
| id | mit-1721.1/132875 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:08:32Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1328752022-01-13T07:55:19Z Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling Wilson, Oliver John. Massachusetts Institute of Technology. Engineering and Management Program. System Design and Management Program. Massachusetts Institute of Technology. Engineering and Management Program Engineering and Management Program. System Design and Management Program. 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 2021-10-08T17:10:17Z 2021-10-08T17:10:17Z 2020 Thesis https://hdl.handle.net/1721.1/132875 1263351398 eng 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 118 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Engineering and Management Program. System Design and Management Program. Wilson, Oliver John. Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title | Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title_full | Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title_fullStr | Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title_full_unstemmed | Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title_short | Machine learning for well rate estimation : integrated imputation and stacked ensemble modeling |
| title_sort | machine learning for well rate estimation integrated imputation and stacked ensemble modeling |
| topic | Engineering and Management Program. System Design and Management Program. |
| url | https://hdl.handle.net/1721.1/132875 |
| work_keys_str_mv | AT wilsonoliverjohn machinelearningforwellrateestimationintegratedimputationandstackedensemblemodeling |