dc.contributor.author |
Abrasaldo, Paul Michael B |
|
dc.contributor.author |
Zarrouk, Sadiq J |
|
dc.contributor.author |
Kempa-Liehr, Andreas W |
|
dc.date.accessioned |
2024-02-06T19:55:41Z |
|
dc.date.available |
2024-02-06T19:55:41Z |
|
dc.date.issued |
2023-07 |
|
dc.identifier.citation |
(2023). Geothermics, 112, 102756-. |
|
dc.identifier.issn |
0375-6505 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/67324 |
|
dc.description.abstract |
The heterogeneous nature of many geothermal resources can be observed in existing geothermal operations when an anomalously low permeability well is drilled amidst high permeability wells. This study looked at a low permeability, weak production well that shared a two-phase pipeline header and separator vessel with several stronger production wells on the same well pad. The utilisation history of the well under investigation had shown episodes wherein the well stopped producing due to its low discharge pressure, even at throttled conditions. Such episodes usually lead to additional costs for the power plant operator as the well has to be stimulated to reinitiate its discharge. Real-time wellhead pressure data was used in this work to develop classification models that would reliably predict the occurrence of such low discharge pressure events in the future. A good performing model would allow the operator to perform intervening measures to prevent the complete collapse of the well, thereby minimising well downtime and avoiding unnecessary costs. A workflow based on systematic time-series feature engineering was applied to characterise the dynamics of the wellhead pressure trend. Machine learning models built using the relevant time-series features were able to reliably predict the occurrence of the low discharge pressure events in the target well. A numerical reservoir model was developed and calibrated to match the transient pressure response and production history of the target well. The model results showed significant fluctuations in the fractional dimension parameter before and after the low discharge pressure events. |
|
dc.language |
en |
|
dc.publisher |
Elsevier |
|
dc.relation.ispartofseries |
Geothermics |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
3707 Hydrology |
|
dc.subject |
37 Earth Sciences |
|
dc.subject |
Science & Technology |
|
dc.subject |
Technology |
|
dc.subject |
Physical Sciences |
|
dc.subject |
Energy & Fuels |
|
dc.subject |
Geosciences, Multidisciplinary |
|
dc.subject |
Geology |
|
dc.subject |
Geothermal energy |
|
dc.subject |
Machine learning |
|
dc.subject |
Feature engineering |
|
dc.subject |
Time-series analytics |
|
dc.subject |
Fractional dimension |
|
dc.subject |
Reservoir modelling |
|
dc.subject |
NEURAL-NETWORK |
|
dc.subject |
SYSTEM |
|
dc.subject |
FIELDS |
|
dc.subject |
0403 Geology |
|
dc.subject |
0404 Geophysics |
|
dc.subject |
0914 Resources Engineering and Extractive Metallurgy |
|
dc.subject |
3705 Geology |
|
dc.subject |
3706 Geophysics |
|
dc.subject |
4019 Resources engineering and extractive metallurgy |
|
dc.title |
Characterising and predicting low discharge pressure events in less permeable geothermal production wells |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.geothermics.2023.102756 |
|
pubs.begin-page |
102756 |
|
pubs.volume |
112 |
|
dc.date.updated |
2024-01-28T22:58:33Z |
|
dc.rights.holder |
Copyright: The authors |
en |
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RetrictedAccess |
en |
pubs.subtype |
Article |
|
pubs.subtype |
Journal |
|
pubs.elements-id |
963124 |
|
pubs.org-id |
Engineering |
|
pubs.org-id |
Engineering Science |
|
dc.identifier.eissn |
1879-3576 |
|
pubs.number |
102756 |
|
pubs.record-created-at-source-date |
2024-01-29 |
|