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ISBN10: 6209466249
ISBN13: 9786209466243
Publisher: Verlag Unser Wissen
Published: Dec 30 2025
Pages: 88
Weight: 0.28
Height: 0.21 Width: 6.00 Depth: 9.00
Language: German
ISBN13: 9786209466243
Publisher: Verlag Unser Wissen
Published: Dec 30 2025
Pages: 88
Weight: 0.28
Height: 0.21 Width: 6.00 Depth: 9.00
Language: German
Das Ziel dieser Forschungsarbeit ist die Entwicklung eines mathematischen Modells als Reservoir-Schätzungswerkzeug für natürlich frakturierte Reservoirs mit doppelten seitlichen Bohrlochkonfigurationen. Das in dieser Studie vorgeschlagene Werkzeug umfasst ein vorwärtsgerichtetes künstliches neuronales Netzwerk (ANN) mit der Fähigkeit, Produktionsdaten anhand bekannter Reservoir- und Bohrlochdesignparameter vorherzusagen. Das vorgeschlagene Werkzeug umfasst auch eine inverse ANN-Komponente, mit der die Permeabilität und Porosität der Matrix und der Fraktur sowie der Fraktur-Abstand und die Reservoirdicke vorhergesagt werden können. Mit Hilfe des vorgeschlagenen Werkzeugs wäre der Benutzer in der Lage, sofort vorhergesagte Reservoir- oder Produktionsdaten mit weniger Kosten und Zeitaufwand zu analysieren. Die zur Entwicklung des Tools verwendete Software umfasste MATLAB, EXCEL und eine kommerzielle Modellierungssoftware. Die Verfahren werden in den folgenden Kapiteln vorgestellt und erläutert, darunter die Generierung von Trainingsdaten, die Auswahl von Trainingsdatensätzen sowie das Training von vorwärtsgerichteten und inversen ANN-Modellen. Darüber hinaus wurde für jedes ANN eine grafische Benutzeroberfläche (GUI) entwickelt und zusammengestellt, über die der Benutzer die Ergebnisse in numerischer und grafischer Form anzeigen kann.
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