PNN Enhanced Seismic Inversion for Porosity Modeling and Delineating the Potential Heterogeneous Gas Sands via Comparative Inversion Analysis in the Lower Indus Basin

Abstract

Seismic inversion has been in use for the last two decades to measure inverted impedances using an integrated data set approach. This research focuses on the application of multi-attribute seismic inversion and the geostatistical probabilistic neural network (PNN) approach for determining rock properties and litho-fluid classification in the Mehar-Mazarani Field of the Lower Indus Basin ( LIB ), Pakistan. The study compares five different inversion techniques, including model-based inversion (MBI), colored inversion (CI), linear sparse spike inversion (LSSI), band-limited inversion (BLI), and maximum likelihood sparse spike inversion (MLSSI). The inverted outputs, such as acoustic P-impedance (Zp), density (ρ), porosity (φ), and shale volume (Vsh), were analyzed in Paleocene and Cretaceous geological complex reservoirs to identify gas-bearing zones. The results indicated the existence of gas between 1630 and 1700 ms (ms) and corresponding depth ranges from approximately 3200 m up to 4200 m with varying thickness. Amongst the inversion techniques, MBI demonstrated greater accuracy, with inverted density volumes showing a strong correlation coefficient of 0.98 and the lowest root mean square error (RMSE) and relative error of 0.10 m/s * g/cc. A geostatistical PNN approach was employed to estimate variations in Vsh and φ within the sand reservoir. MBI again yielded more reliable results, with a strong correlation between the measured and inverted attributes. High φ and low Vsh were observed in predetermined low-impedance zones. Overall, MBI is proven to be the most accurate and reliable technique, providing clear identification of the gas occurrence. This research highlights the effectiveness of seismic inversion, particularly the application of MBI, in determining rock properties and identifying gas-bearing zones within the Mehar-Mazarani gas field.

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