Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems
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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems
Prediction Models Exploiting Well-Log Information
Wood, David A.
Elsevier - Health Sciences Division
01/2025
475
Mole
9780443265105
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1. Regression models to estimate total organic carbon (TOC) from well-log data
2. Predicting brittleness indexes in tight formation sequences
3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences
4. Permeability and water saturation distributions in complex reservoirs
5. Trapping mechanisms in potential sub-surface carbon storage reservoirs
6. The accurate picking of formation tops in field development wells
7. Assessing formation loss of circulation risks with mud-log datasets
8. Delineating fracture densities and apertures using well-log image data
9. Determining reservoir microfacies using photomicrograph and computed tomography image data
10. Characterizing coal-bed methane reservoirs with well-log datasets
2. Predicting brittleness indexes in tight formation sequences
3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences
4. Permeability and water saturation distributions in complex reservoirs
5. Trapping mechanisms in potential sub-surface carbon storage reservoirs
6. The accurate picking of formation tops in field development wells
7. Assessing formation loss of circulation risks with mud-log datasets
8. Delineating fracture densities and apertures using well-log image data
9. Determining reservoir microfacies using photomicrograph and computed tomography image data
10. Characterizing coal-bed methane reservoirs with well-log datasets
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
(CNN) residual units; (CNN) skip/skipless connections; 1D convolutional encoder; 1D panoramic stitching; 2-D and 3-D partial dependence plots; 2D porosity / permeability; 2D windowed well-log images; AFr-index prediction metrics; AUC(Tr)/AUC(Ts) ratio; Absolute versus effective permeability; Adaptive synthetic sampling approach (ADASYN;) lost circulation fluid losses; Additive feature attribution; Adsorbed gas; Annotated confusion matrix; Appalachian Basin; Area under curve (AUC); Attention U-Net; Autocorrelation; Automated fracture detection; Automated well tops; Average dice coefficient; Balanced accuracy; Barnett Shale; Base learner models; Binary classification configuration; Bootstrapping; Borehole imaging log; Calculated well-log attributes; Capillary pressure curves; Carbon capture and sequestration (CCS); Carbon capture utilization and sequestration (CCUS) proxy model; Chi-squared p-test; Class imbalance; Classification misclassification depth plots; Cluster analysis; Cluster-based undersampling; Coal as a carbon dioxide storage medium; Coal bed methane; Coal lithology from well logs; Coal mine dynamic failures; Coal mine gas outburst risk; Coal seam gas emissions; Coal-related surface contamination; Committee-decision combinations; Confocal-laser-scanning microscopy; Control parameter optimization; Convolutional architectures; Convolutional neural networks; Cook's distance; Core-plug scanned images; Customized formula optimization; Data analytics; Data augmentation; Data characterization; Data matching SML models; Data mining; Data normalization; Data upscaling; Dataset imbalance; Decision logic classifier; Deep learning; Deep reinforcement learning; Delta-log-resistivity method; Derivative / volatility well-log attributes; Dice score coefficient; Difference of fits (DFFITS); Dimensionality reduction; Dipole Shear Sonic Imager (DSI); Distances from lithofacies boundaries; Dynamic time warping; Elbow plot; Empirical TOC prediction methods; Enhanced deep residual neural networks; Ensemble model combinatioons; F1-score; FIB-SEM; Fast/slow shear wave analysis; Fe-SEM; Feature augmentation; Feature importance; Feature selection; Flow zone index; Formation microimager (FMI); Formation-boundary picking; Formula optimized classifier; Fracability; Fracture aperture dimensions; Fracture potential evaluation; Fractured carbonate reservoir; Fractured carbonate reservoirs; F?-score; Gamma correction; Gas calorific value; Gas production rate prediction; Gas-sorption time; Gaussian probability density function; Generalized machine learning stacking model; Geographic quantile regression forest interpolation; Geologic carbon storage; Geomechanical brittlenes
1. Regression models to estimate total organic carbon (TOC) from well-log data
2. Predicting brittleness indexes in tight formation sequences
3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences
4. Permeability and water saturation distributions in complex reservoirs
5. Trapping mechanisms in potential sub-surface carbon storage reservoirs
6. The accurate picking of formation tops in field development wells
7. Assessing formation loss of circulation risks with mud-log datasets
8. Delineating fracture densities and apertures using well-log image data
9. Determining reservoir microfacies using photomicrograph and computed tomography image data
10. Characterizing coal-bed methane reservoirs with well-log datasets
2. Predicting brittleness indexes in tight formation sequences
3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences
4. Permeability and water saturation distributions in complex reservoirs
5. Trapping mechanisms in potential sub-surface carbon storage reservoirs
6. The accurate picking of formation tops in field development wells
7. Assessing formation loss of circulation risks with mud-log datasets
8. Delineating fracture densities and apertures using well-log image data
9. Determining reservoir microfacies using photomicrograph and computed tomography image data
10. Characterizing coal-bed methane reservoirs with well-log datasets
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
(CNN) residual units; (CNN) skip/skipless connections; 1D convolutional encoder; 1D panoramic stitching; 2-D and 3-D partial dependence plots; 2D porosity / permeability; 2D windowed well-log images; AFr-index prediction metrics; AUC(Tr)/AUC(Ts) ratio; Absolute versus effective permeability; Adaptive synthetic sampling approach (ADASYN;) lost circulation fluid losses; Additive feature attribution; Adsorbed gas; Annotated confusion matrix; Appalachian Basin; Area under curve (AUC); Attention U-Net; Autocorrelation; Automated fracture detection; Automated well tops; Average dice coefficient; Balanced accuracy; Barnett Shale; Base learner models; Binary classification configuration; Bootstrapping; Borehole imaging log; Calculated well-log attributes; Capillary pressure curves; Carbon capture and sequestration (CCS); Carbon capture utilization and sequestration (CCUS) proxy model; Chi-squared p-test; Class imbalance; Classification misclassification depth plots; Cluster analysis; Cluster-based undersampling; Coal as a carbon dioxide storage medium; Coal bed methane; Coal lithology from well logs; Coal mine dynamic failures; Coal mine gas outburst risk; Coal seam gas emissions; Coal-related surface contamination; Committee-decision combinations; Confocal-laser-scanning microscopy; Control parameter optimization; Convolutional architectures; Convolutional neural networks; Cook's distance; Core-plug scanned images; Customized formula optimization; Data analytics; Data augmentation; Data characterization; Data matching SML models; Data mining; Data normalization; Data upscaling; Dataset imbalance; Decision logic classifier; Deep learning; Deep reinforcement learning; Delta-log-resistivity method; Derivative / volatility well-log attributes; Dice score coefficient; Difference of fits (DFFITS); Dimensionality reduction; Dipole Shear Sonic Imager (DSI); Distances from lithofacies boundaries; Dynamic time warping; Elbow plot; Empirical TOC prediction methods; Enhanced deep residual neural networks; Ensemble model combinatioons; F1-score; FIB-SEM; Fast/slow shear wave analysis; Fe-SEM; Feature augmentation; Feature importance; Feature selection; Flow zone index; Formation microimager (FMI); Formation-boundary picking; Formula optimized classifier; Fracability; Fracture aperture dimensions; Fracture potential evaluation; Fractured carbonate reservoir; Fractured carbonate reservoirs; F?-score; Gamma correction; Gas calorific value; Gas production rate prediction; Gas-sorption time; Gaussian probability density function; Generalized machine learning stacking model; Geographic quantile regression forest interpolation; Geologic carbon storage; Geomechanical brittlenes
