Low Temperature Geothermal Play Fairway Analysis For The Appalachian Basin: Phase 1 Revised Report November 18, 2016

Jordan, Teresa E.; Richards, Maria; Horowitz, Franklin G.; Camp, Erin; Smith, J. D.; Whealton, Calvin; Stedinger, Jery R.; Hornbach, Matthew J.; Frone, Zachary; Tester, Jefferson W.; Anderson, Brian; Welcker, Kelydra; Pace, Catherine Chickering; He, Xiaoning; Magnani, M. B.; Bolat, Rahmi

doi:10.2172/1341349

Cited by 12 publications

(17 citation statements)

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Section: Introductionmentioning

confidence: 99%

“…Jordan et al (Jordan, T.E., 2016), conducted a thorough analysis to explore the associated risks and potentials of prospective geothermal resources in New York, Pennsylvania and West Virginia States. Even though most geothermally active regions are located in the west of United States (near Earth's tectonic plate boundaries), Jordan et al (Jordan, T.E., 2016) showed that the stored energy in the low-temperature geothermal regions in the northeast could be utilized for many direct-use applications. Even though Snyder et al (Snyder et al, 2017) illustrated that myriad industrial and residential direct-use applications of geothermal energy could result in reduction of electricity consumption, there are not many geothermal sites in northeastern states due to a high financial risk.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous geothermal studies, the generalized thermal conductivity model has been adopted to compute the heat flow associated with BHT data points (Blackwell and Richards, 2010;Frone and Blackwell, 2010;Jordan, T.E., 2016;Jordan, 2015;Stutz et al, 2012;Tester et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Exploratory Analysis of Machine Learning Methods in Predicting Subsurface Temperature and Geothermal Gradient of Northeastern United States

Shahdi

Lee

Karpatne

et al. 2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploratory Analysis of Machine Learning Methods in Predicting Subsurface Temperature and Geothermal Gradient of Northeastern United States

Shahdi

Lee

Karpatne

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…flux and temperature-at-depth maps. Jordan et al (2016) conducted a thorough analysis to explore the associated risks and potentials of prospective geothermal resources in the states of New York, Pennsylvania and West Virginia. Even though most geothermally active regions are located in the western United States (near Earth's tectonic plate boundaries), Jordan et al (2016) showed that the stored energy in the low-temperature geothermal regions in the northeast could be utilized for many direct-use applications.…”

mentioning

confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

View full text Add to dashboard Cite

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 and Random Forest result in the highest accuracy for subsurface temperature prediction. 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 58 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, through our analysis, the most favorable geological locations are suggested for potential future geothermal developments.

show abstract

“…Projects shown in bold continued on to Phase 2. DOE-funded effort applied the Play Fairway Analysis (PFA) approach to low-temperature geothermal exploration and potential development of direct-use geothermal plays in the Appalachian Basin portions of New York, Pennsylvania, and West Virginia(Jordan et al, 2016). Broad regions of the basin in all three states have heat fluxes in excess of 50 mW/m 2 .…”

mentioning

confidence: 99%

GeoVision Analysis: Exploration Task Force Report

Doughty

Dobson

Wall

et al. 2018

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Low Temperature Geothermal Play Fairway Analysis For The Appalachian Basin: Phase 1 Revised Report November 18, 2016

Cited by 12 publications

References 0 publications

Exploratory Analysis of Machine Learning Methods in Predicting Subsurface Temperature and Geothermal Gradient of Northeastern United States

Exploratory Analysis of Machine Learning Methods in Predicting Subsurface Temperature and Geothermal Gradient of Northeastern United States

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

GeoVision Analysis: Exploration Task Force Report

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