| Titre : |
Predictive and comparative study of petrophysical parameters based on AI |
| Type de document : |
document électronique |
| Auteurs : |
Karine-Anais Imadalou, Auteur ; Aya-Fella Mimouni, Auteur ; Larouci Chanane, Directeur de thèse ; Aziz khelalef, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (19.7 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Minier : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 130-134 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Machine learning
Artificial intelligence
Prediction
Clay volume
Effective
Porosity
Water saturation
Logs
Reservoirs
Berkine Basin |
| Index. décimale : |
PG00325 |
| Résumé : |
This study aims to explore the use of machine learning as a powerful artificial intelligence tool to develop an algorithm capable of estimating and predicting three essential petro-
physical parameters: clay volume (VCL), effective porosity (P HIE), and water saturation (SW ), based on raw log data from several production wells in the Berkine Basin. The
main challenge lies in the accurate prediction of water saturation. Several models were compared, including XGBoost, MLP, and CNN. The results obtained, especially with the CNN model, demonstrate the high efficiency of machine learning techniques, achiev-ing a global determination coefficient of R2 = 0.81 for water saturation, which is the most complex parameter to predict. |
Predictive and comparative study of petrophysical parameters based on AI [document électronique] / Karine-Anais Imadalou, Auteur ; Aya-Fella Mimouni, Auteur ; Larouci Chanane, Directeur de thèse ; Aziz khelalef, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (19.7 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Minier : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 130-134 Langues : Anglais ( eng)
| Mots-clés : |
Machine learning
Artificial intelligence
Prediction
Clay volume
Effective
Porosity
Water saturation
Logs
Reservoirs
Berkine Basin |
| Index. décimale : |
PG00325 |
| Résumé : |
This study aims to explore the use of machine learning as a powerful artificial intelligence tool to develop an algorithm capable of estimating and predicting three essential petro-
physical parameters: clay volume (VCL), effective porosity (P HIE), and water saturation (SW ), based on raw log data from several production wells in the Berkine Basin. The
main challenge lies in the accurate prediction of water saturation. Several models were compared, including XGBoost, MLP, and CNN. The results obtained, especially with the CNN model, demonstrate the high efficiency of machine learning techniques, achiev-ing a global determination coefficient of R2 = 0.81 for water saturation, which is the most complex parameter to predict. |
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