Abstract
The main objective of this study is to characterize the potential reservoirs situated in Oued Mya basin in Algeria by using pre-stack seismic inversion data. In fact, rock physics has been used to estimate shear waves and their densities from the compressional wave velocities at six wells, and then, cross-plots were exploited to establish a mathematical relation between the petrophysical and acoustic parameters of the rocks. After that, the lithological classification has been iteratively generalized for the whole seismic volume. The final model, obtained with a correlation ratio of 75%, allowed the classification of the dominant facies in the reservoir. Furthermore, the Petrophysical volumes obtained using these mathematical relations provided the horizontal distribution of the different existing reservoirs with a focus on the potential ones. The obtained results in this case study have highlighted the crucial role played by the seismic inversion in the characterization of the oil reservoirs in the Oued Mya Basin. It also permitted to provide lateral variations of petrophysical parameters of the reservoir; thus, it eliminates the problem of punctuality of the information provided by logging data. This characterization step is very important for determining the positions of new exploration drillings in order to optimize exploration strategy with minimal uncertainties; therefore, exploration costs can be optimized. The main novelty of this paper is that the obtained model was very reliable and its correlations with six wells in the basin were high; hence, it has been used to characterize the reservoirs and to identify new areas with hydrocarbons potentials where highly probable discoveries were identified.
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Eladj, S., Benabid, M., Doghmane, M.Z. (2024). Pre-stack Seismic Inversion for Potential Reservoirs’ Characterization in Oued Mya Basin, Algeria. In: Khomsi, S., et al. Selected Studies in Geophysics, Tectonics and Petroleum Geosciences. CAJG 2020. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-43807-3_16
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