Abstract
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.
Highlights
Bottom-hole temperature (BHT) measurements have largely been used for mapping subsurface temperatures for geothermal resource analysis across the United States (Blackwell and Richards 2010; Frone and Blackwell 2010; Stutz et al 2012; Tester et al 2006).BHT data are predominantly provided by oil and gas wells, where maximum temperature is usually reported at the final drilled depth
We provide an alternative solution of using machine learning methods for predicting subsurface temperature using BHT data from more than 20,750 oil and gas wells in the northeastern United States
It is critical to understand that this paper does not claim to prove that machine learning models are ubiquitously superior to conventional physics-based models in geothermal energy research
Summary
Bottom-hole temperature (BHT) measurements have largely been used for mapping subsurface temperatures for geothermal resource analysis across the United States (Blackwell and Richards 2010; Frone and Blackwell 2010; Stutz et al 2012; Tester et al 2006).BHT data are predominantly provided by oil and gas wells, where maximum temperature is usually reported at the final drilled depth. Bottom-hole temperature (BHT) measurements have largely been used for mapping subsurface temperatures for geothermal resource analysis across the United States (Blackwell and Richards 2010; Frone and Blackwell 2010; Stutz et al 2012; Tester et al 2006). Incorporated BHT data in northeastern United States with stratigraphic information (Childs 1985), and used a simple thermal conductivity model to generate surface heat. 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. 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. Heat flux and temperature-at-depth are two most important geothermal parameters, which have extensively been investigated through physics-based models
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