Abstract
The seismic data inversion provides litho-stratigraphic information necessary for the reservoir characterization and new traps discoveries. However, uncertainties inherent in seismic data inversion and nonlinear relationship between petrophysical parameters pose a challenge for reliable reservoir characterization. In this study, a multilayer feed-forward neural network (MLFN) is designed to overcome the non-uniqueness of the seismic inversion solution. MLFN learning was based on the logging data. The 3D seismic acoustic was inverted using the colored inversion. The resulting acoustic impedance volume was then used as an input for model-based inversion method designed for calculating the porosity volume using the trained artificial neural network. The effectiveness of the proposed algorithm was demonstrated using Algerian hydrocarbons field.
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Eladj, S., Doghmane, M.Z., Aliouane, L., Ouadfeul, SA. (2022). Porosity Model Construction Based on ANN and Seismic Inversion: A Case Study of Saharan Field (Algeria). In: Meghraoui, M., et al. Advances in Geophysics, Tectonics and Petroleum Geosciences. CAJG 2019. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-73026-0_55
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