Matches in SemOpenAlex for { <https://semopenalex.org/work/W4256149946> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4256149946 abstract "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." @default.
- W4256149946 created "2022-05-12" @default.
- W4256149946 creator A5000341704 @default.
- W4256149946 creator A5077928172 @default.
- W4256149946 creator A5081622450 @default.
- W4256149946 creator A5084176998 @default.
- W4256149946 date "2021-01-05" @default.
- W4256149946 modified "2023-09-26" @default.
- W4256149946 title "Exploratory Analysis of Machine Learning Methods in Predicting Subsurface Temperature and Geothermal Gradient of Northeastern United States" @default.
- W4256149946 doi "https://doi.org/10.21203/rs.3.rs-131433/v1" @default.
- W4256149946 hasPublicationYear "2021" @default.
- W4256149946 type Work @default.
- W4256149946 citedByCount "1" @default.
- W4256149946 countsByYear W42561499462022 @default.
- W4256149946 crossrefType "posted-content" @default.
- W4256149946 hasAuthorship W4256149946A5000341704 @default.
- W4256149946 hasAuthorship W4256149946A5077928172 @default.
- W4256149946 hasAuthorship W4256149946A5081622450 @default.
- W4256149946 hasAuthorship W4256149946A5084176998 @default.
- W4256149946 hasBestOaLocation W42561499461 @default.
- W4256149946 hasConcept C105795698 @default.
- W4256149946 hasConcept C111766609 @default.
- W4256149946 hasConcept C119857082 @default.
- W4256149946 hasConcept C121332964 @default.
- W4256149946 hasConcept C124101348 @default.
- W4256149946 hasConcept C127313418 @default.
- W4256149946 hasConcept C137109543 @default.
- W4256149946 hasConcept C139945424 @default.
- W4256149946 hasConcept C153294291 @default.
- W4256149946 hasConcept C154945302 @default.
- W4256149946 hasConcept C204530211 @default.
- W4256149946 hasConcept C205649164 @default.
- W4256149946 hasConcept C2985596519 @default.
- W4256149946 hasConcept C33923547 @default.
- W4256149946 hasConcept C41008148 @default.
- W4256149946 hasConcept C518406490 @default.
- W4256149946 hasConcept C8058405 @default.
- W4256149946 hasConcept C97346530 @default.
- W4256149946 hasConcept C97355855 @default.
- W4256149946 hasConceptScore W4256149946C105795698 @default.
- W4256149946 hasConceptScore W4256149946C111766609 @default.
- W4256149946 hasConceptScore W4256149946C119857082 @default.
- W4256149946 hasConceptScore W4256149946C121332964 @default.
- W4256149946 hasConceptScore W4256149946C124101348 @default.
- W4256149946 hasConceptScore W4256149946C127313418 @default.
- W4256149946 hasConceptScore W4256149946C137109543 @default.
- W4256149946 hasConceptScore W4256149946C139945424 @default.
- W4256149946 hasConceptScore W4256149946C153294291 @default.
- W4256149946 hasConceptScore W4256149946C154945302 @default.
- W4256149946 hasConceptScore W4256149946C204530211 @default.
- W4256149946 hasConceptScore W4256149946C205649164 @default.
- W4256149946 hasConceptScore W4256149946C2985596519 @default.
- W4256149946 hasConceptScore W4256149946C33923547 @default.
- W4256149946 hasConceptScore W4256149946C41008148 @default.
- W4256149946 hasConceptScore W4256149946C518406490 @default.
- W4256149946 hasConceptScore W4256149946C8058405 @default.
- W4256149946 hasConceptScore W4256149946C97346530 @default.
- W4256149946 hasConceptScore W4256149946C97355855 @default.
- W4256149946 hasLocation W42561499461 @default.
- W4256149946 hasLocation W42561499462 @default.
- W4256149946 hasOpenAccess W4256149946 @default.
- W4256149946 hasPrimaryLocation W42561499461 @default.
- W4256149946 hasRelatedWork W1987003780 @default.
- W4256149946 hasRelatedWork W20364724 @default.
- W4256149946 hasRelatedWork W2040794427 @default.
- W4256149946 hasRelatedWork W2048354209 @default.
- W4256149946 hasRelatedWork W2049826873 @default.
- W4256149946 hasRelatedWork W2921341843 @default.
- W4256149946 hasRelatedWork W3018455094 @default.
- W4256149946 hasRelatedWork W3019562943 @default.
- W4256149946 hasRelatedWork W3097148960 @default.
- W4256149946 hasRelatedWork W3137508460 @default.
- W4256149946 isParatext "false" @default.
- W4256149946 isRetracted "false" @default.
- W4256149946 workType "article" @default.