Towards better shale gas production forecasting using transfer learning
Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the...
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| Published in | Upstream Oil and Gas Technology Vol. 9; p. 100072 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2666-2604 2666-2604 |
| DOI | 10.1016/j.upstre.2022.100072 |
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| Abstract | Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the county of interest. The paper uses data from more than 6000 shale gas wells across 17 counties from Texas Barnett and Pennsylvania Marcellus shale formations to test the capabilities of transfer learning. The results reduce the forecasting error between 11% and 47% compared to the widely used Arps decline curve model. |
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| AbstractList | Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the county of interest. The paper uses data from more than 6000 shale gas wells across 17 counties from Texas Barnett and Pennsylvania Marcellus shale formations to test the capabilities of transfer learning. The results reduce the forecasting error between 11% and 47% compared to the widely used Arps decline curve model. |
| ArticleNumber | 100072 |
| Author | Alolayan, Omar S. Montgomery, Justin B. Williams, John R. Raymond, Samuel J. |
| Author_xml | – sequence: 1 givenname: Omar S. orcidid: 0000-0002-1542-6253 surname: Alolayan fullname: Alolayan, Omar S. email: olyanos@mit.edu organization: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, United States – sequence: 2 givenname: Samuel J. orcidid: 0000-0003-2691-652X surname: Raymond fullname: Raymond, Samuel J. organization: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, United States – sequence: 3 givenname: Justin B. surname: Montgomery fullname: Montgomery, Justin B. organization: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, United States – sequence: 4 givenname: John R. surname: Williams fullname: Williams, John R. organization: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, United States |
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| Cites_doi | 10.3390/en11030552 10.1073/pnas.1313380110 10.3390/en13112765 10.1016/j.jngse.2015.03.025 10.1090/qam/10666 10.1016/j.upstre.2020.100022 10.2118/08-07-57 10.1109/JPROC.2020.3004555 10.2118/192046-MS 10.1137/0111030 10.1016/j.apenergy.2017.03.038 |
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| Keywords | Decline curve analysis Shale gas forecasting County-specific models Transfer learning Machine learning |
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Energy doi: 10.1016/j.apenergy.2017.03.038 |
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| StartPage | 100072 |
| SubjectTerms | County-specific models Decline curve analysis Machine learning Shale gas forecasting Transfer learning |
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| Title | Towards better shale gas production forecasting using transfer learning |
| URI | https://dx.doi.org/10.1016/j.upstre.2022.100072 https://www.sciencedirect.com/science/article/pii/S266626042200010X |
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