An integrated framework for production data analysis using machine learning and wavelets
Abstract/Contents
- Abstract
- 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.
Description
| 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 |
Creators/Contributors
| 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 |
Subjects
| 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. |
| Location | https://purl.stanford.edu/cd505vg2362 |
Access conditions
- Copyright
- © 2022 by Dante Isaac Orta Aleman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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