| Titre : | AAPG Bulletin, N°11 vol.106 - november 2022 |
| Type de document : | Bulletin : Revue |
| Paru le : | 01/11/2022 |
| Année de publication : | 2022 |
| Format : | tout le numéro |
| Langues: | Anglais |
| Concepts : |
GEOSCIENCES
GEOLOGIE RESERVOIR HYDROGEOLOGIE MACHINE SISMOLOGIE PETROPHYSIQUE LITHOFACIES |
| Note de contenu : |
Introduction to Special Issue: Geoscience Data Analytics and Machine Learning
Three common statistical missteps we make in reservoir characterization Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potentia Unconventional reservoir characterization by seismic inversion and machine learning of the Bakken Formation A cross-shape deep Boltzmann machine for petrophysical seismic inversion Application of random forest algorithm to predict lithofacies from well and seismic data in Balder field, Norwegian North Sea Deep convolutional neural networks for generating grain-size logs from core photographs Shale brittleness prediction using machine learning—A Middle East basin case study Improving total organic carbon estimation for unconventional shale reservoirs using Shapley value regression and deep machine learning methods A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data |
Exemplaires (1)
| Cote | Support | Section | Disponibilité |
|---|---|---|---|
| PER AAP | Revue | Réserve | Disponible |
