Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach

Optimizing petrophysical property prediction in fluvial-deltaic reservoirs: a multi-seismic attribute transformation and probabilistic neural network approach

Abstract Conventional techniques, which depend on geostatistical modeling, frequently fail to capture reservoir variability, especially when well data are sparse. To overcome this limitation, we develop a combined approach that integrates Multi-Seismic Attribute Transformation (MSAT) and Probabilist...

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Bibliographic Details
Main Authors: Muhammad Khan, Andy Anderson Bery, Yasir Bashir, Sya’rawi Muhammad Husni Sharoni, Joseph Gnapragasan, Qazi Sohail Imran
Format: Article
Language:English
Published: SpringerOpen 2025-02-01
Series:Journal of Petroleum Exploration and Production Technology
Subjects:
Probabilistic neural network
Multi-seismic attribute transformation
Dip-steer median filter
Seismic attributes
Porosity prediction
Seismic inversion
Online Access:https://doi.org/10.1007/s13202-024-01912-6
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https://doi.org/10.1007/s13202-024-01912-6

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