A machine learning approach to predict drilling rate using petrophysical and mud logging data

Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Earth science informatics 2019-09, Vol.12 (3), p.319-339
Hauptverfasser:	Sabah, Mohammad, Talebkeikhah, Mohsen, Wood, David A., Khosravanian, Rasool, Anemangely, Mohammad, Younesi, Alireza
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Artificial neural networks Basis functions Data logging Data mining Drilling machines (tools) Drilling muds Drilling rate Earth and Environmental Science Earth Sciences Earth System Sciences Flow rates Flow velocity Flow-density-speed relationships Fluid flow Gamma rays Genetic algorithms Geology Information Systems Applications (incl.Internet) Learning theory Machine learning Mathematical models Multilayers Neural networks Ontology Particle swarm optimization Pore pressure Pore water pressure Predictions Radial basis function Research Article Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Support vector machines Wave velocity Weight
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!