An integrated framework for production data analysis using machine learning and wavelets

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This work introduces a framework for generating full data-driven well models that combine machine and deep learning algorithms with wavelet-based decompositions. The research shows that applying the Maximum Overlap Discrete Wavelet Transform Multiresolution Analysis (MODWT-MRA) to pressure and flow rate data from a well results in a decomposition analogous to a set of superposed wells in space and time. Two specific applications are presented in the work, capturing a well's pressure response from flow rate data, and reconstructing the flow rate history. The methodology increased model accuracy and noise filtering as well as allowed for the use of incomplete datasets with minimum loss of performance.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English


Author Orta Alemán, Dante Isaac
Degree supervisor Horne, Roland N
Thesis advisor Horne, Roland N
Thesis advisor Mukerji, Tapan, 1965-
Thesis advisor Tartakovsky, Daniel
Degree committee member Mukerji, Tapan, 1965-
Degree committee member Tartakovsky, Daniel
Associated with Stanford University, Department of Energy Resources Engineering


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Dante Isaac Orta Alemán.
Note Submitted to the Department of Energy Resources Engineering.
Thesis Thesis Ph.D. Stanford University 2022.

Access conditions

© 2022 by Dante Isaac Orta Aleman
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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