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Arya Shahdi
Virginia Polytechnic Institute and State University College of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2328-5147
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Geothermal scientists have used bottom hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost results in the highest accuracy for subsurface temperature prediction with average mean-absolute-error and root-mean-square-error of 3.19[°C] and 4.94[°C], respectively. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for fifty-eight wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost predicted temperatures along the depth. Finally, thorough our analysis, the most favorable geological locations are suggested for potential future geothermal developments.
Keywords
Renewable Energy, Geothermal Energy, Machine Learning, XGBoost, Subsurface temperature, geothermal gradient
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On 20 Jan, 2021
Reviewers invited
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This preprint is under consideration at Geothermal Energy. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Arya Shahdi
Virginia Polytechnic Institute and State University College of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2328-5147
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
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Peer Review Timeline
CURRENT STATUS: Under Review
Version (no preprint)
Posted 31 May, 2021
Editorial decision: Minor revision
On 31 May, 2021
Editor assigned
On 27 May, 2021
Submission checks complete
On 26 May, 2021
Editor invited
On 26 May, 2021
First submitted
On 23 May, 2021
This version was not preprinted
Version (no preprint)
Posted 22 Apr, 2021
Editorial decision: Minor revision
On 22 Apr, 2021
Review #2 received
Received 19 Apr, 2021
Review #1 received
Received 19 Apr, 2021
Reviewer #2 agreed
On 11 Apr, 2021
Reviewers invited
Invitations sent on 06 Apr, 2021
Reviewer #1 agreed
On 06 Apr, 2021
Editor assigned
On 26 Mar, 2021
Submission checks complete
On 26 Mar, 2021
Editor invited
On 26 Mar, 2021
This version was not preprinted
Version 1
Posted 05 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 14 Feb, 2021
Review #1 received
Received 08 Feb, 2021
Review #3 received
Received 05 Feb, 2021
Review #2 received
Received 04 Feb, 2021
Reviewer #3 agreed
On 22 Jan, 2021
Reviewer #2 agreed
On 20 Jan, 2021
Reviewers invited
Invitations sent on 18 Jan, 2021
Reviewer #1 agreed
On 18 Jan, 2021
Editor assigned
On 27 Dec, 2020
Submission checks complete
On 27 Dec, 2020
Editor invited
On 27 Dec, 2020
First submitted
On 26 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Energy Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Geothermal scientists have used bottom hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost results in the highest accuracy for subsurface temperature prediction with average mean-absolute-error and root-mean-square-error of 3.19[°C] and 4.94[°C], respectively. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for fifty-eight wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost predicted temperatures along the depth. Finally, thorough our analysis, the most favorable geological locations are suggested for potential future geothermal developments.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
This preprint is available for download as a PDF.
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