Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

DOI10.1007/s10596-016-9577-0zbMath1349.86019OpenAlexW2395369153MaRDI QIDQ341530

Sadegh Karimpouli, Hadi Fattahi

Publication date: 16 November 2016

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-016-9577-0

zbMATH Keywords

porosityparticle swarm optimizationBayesian inversionsupport vector regressionANFIS-subtractive clustering methodcarbonate reservoirwater saturation

Mathematics Subject Classification ID

Hydrology, hydrography, oceanography (86A05) Flows in porous media; filtration; seepage (76S05) Seismology (including tsunami modeling), earthquakes (86A15)

Uses Software

• ANFIS

Cites Work

• Unnamed Item
• Facing the classification of binary problems with a GSA-SVM hybrid system
• Reservoir Geomechanics
• Long-wave elastic anisotropy produced by horizontal layering
• Neural-network-based fuzzy logic control and decision system

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