scholarly journals Subsurface temperature data from wells north of sixty, Yukon-Northwest Territories

1984 ◽  
Author(s):  
Keyword(s):  
Northwest Territories ◽  
Temperature Data ◽  
Subsurface Temperature

scholarly journals New subsurface temperature maps for the Tertiary Lower Rhine Basin and the adjacent Variscan Basement - Germany, The Netherlands, Belgium

2004 ◽  
Vol 83 (2) ◽  
pp. 135-146 ◽  
Author(s):  
H. Karg ◽  
C. Bücker ◽  
R. Schellschmidt
Keyword(s):  
Temperature Field ◽  
Coal Mine ◽  
Temperature Data ◽  
Depth Interval ◽  
Subsurface Temperature ◽  
Rhine Basin ◽  
Lower Rhine Basin ◽  
Temperature Maps ◽  
Variscan Basement ◽  
Lower Rhine

AbstractThe regional subsurface temperature field at the transition between the Palaeozoic Variscan Basement and the Cenozoic Lower Rhine Basin in Dutch, German and Belgium territories was mapped up to a depth of 1000 m. Temperature data from 66 wells and 11 coal mine subcrops were available. In 46 wells, temperature logs, covering a cumulative depth interval of 6600 m, were measured for this study.

scholarly journals Deep subsurface temperatures in the Roer Valley Graben and the Peelblock, the Netherlands - new results

2002 ◽  
Vol 81 (1) ◽  
pp. 19-26 ◽  
Author(s):  
R.T. Van Balen ◽  
J.M. Verweij ◽  
J.D. van Wees ◽  
H. Simmelink ◽  
F. Van Bergen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Tectonic Evolution ◽  
Late Eocene ◽  
Temperature Data ◽  
Hydrocarbon Exploration ◽  
Subsurface Temperature ◽  
Deep Subsurface ◽  
Roer Valley Graben ◽  
Cold Area ◽  
Temperature Maps

AbstractThe deep subsurface temperature data of the Roer Valley Graben have been re-analysed and combined with new temperature data from hydrocarbon exploration wells. The results show that the deep subsurface temperature distribution in the Roer Valley Graben is essentially the same as in the relatively stable high bordering the Roer Valley Graben to the southwest. Thus, the Cenozoic tectonic evolution of the Roer Valley Graben, which is characterized by uplift and denudation during the Late Eocene and subsidence due to rifting starting from Late Oligocene, has hardly affected the temperatures in the graben, which is probably due to the slow subsidence and sedimentation rates. In contrast to what is suggested on previously published temperature maps, the Roer Valley Graben is probably not a relatively cold area in the Netherlands.

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

2021 ◽  
Author(s):  
Arya Shahdi ◽  
Seho Lee ◽  
Anuj Karpatne ◽  
Bahareh Nojabaei
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Active Regions ◽  
Geothermal Gradient ◽  
Temperature Data ◽  
Exploratory Analysis ◽  
Measured Temperature ◽  
Subsurface Temperature ◽  
Learning Models ◽  
Machine Learning Models

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 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.

scholarly journals Refining the thermal structure of the European lithosphere by inversion of subsurface temperature data

2018 ◽  
Vol 171 ◽  
pp. 18-47 ◽  
Author(s):  
Jon Limberger ◽  
Jan-Diederik van Wees ◽  
Magdala Tesauro ◽  
Jeroen Smit ◽  
Damien Bonté ◽  
...  
Keyword(s):  
Thermal Structure ◽  
Temperature Data ◽  
Subsurface Temperature
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