Title :
Well-Log Acoustic Velocity Prediction Based on Relevance Vector Machine
Author :
Ma, Hai ; Wang, Yanjiang
Author_Institution :
Coll. of Inf. & Control Eng., China Univ. of Pet., Dongying, China
Abstract :
By analyzing the relation between well-log data and seismic data, a novel method for well-log acoustic velocity prediction based on relevance vector machine (RVM) is proposed. The proposed method is applied to the well in Junggar Basin and the experimental results show it has higher prediction accuracy, faster convergence speed and better generalization. Through this algorithm, we can obtain high resolution well-log acoustic velocity profile and improve the drilling simulation quality and drilling engineering design level.
Keywords :
acoustic wave velocity; drilling (geotechnical); learning (artificial intelligence); drilling engineering design level; drilling simulation quality; relevance vector machine; seismic data; well-log acoustic velocity prediction; well-log data; Bayesian methods; Computer networks; Control engineering; Data mining; Drilling; Educational institutions; Geophysics computing; Information analysis; Kernel; Petroleum; prediction; relevance vector machine; seismic; well-log acoustic velocity;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.147