The assessment of model-based inversion and petrophysical analysis for calculating porosity using PNN in the early cretaceous B-sand reservoir

Rapid demand for energy has led to an increase in new techniques for hydrocarbon exploration to avoid uncertainty. Evaluating hydrocarbon potential and estimating porosity is a persistent challenge using seismic and well log data in reservoirs. Seismic post-stacked inversion is a robust method for detailed reservoir characterization that produces more precise and comprehensive subsurface images than traditional seismic analysis alone. This article presents a case study for exploration and development by assisting in identifying and understanding hydrocarbon-bearing zones, such as the B-sand reservoir, the early Cretaceous-aged reservoirs in the Lower Indus Basin, Pakistan, using integrated 3D seismic and well log data. To estimate the porosity of the reservoir by post-stack seismic inversion, the technique is followed by petrophysical analysis, estimation of static wavelet, low-frequency model, and model-based inversion. The objective of this research is to predict the porosity and map the porosity distribution of the B-sand reservoir by converting seismic reflection data into acoustic impedance values. These objectives are attained by employing a porosity and P-impedance cross plot and then incorporating seismic interpretation and model-based inversion (MBI) for enhanced accuracy and certainty. For encouraging MBI results, a wavelet representative of the specified condition must be appropriate. According to MBI outcomes, low acoustic impedance values were found, indicating the existence of good hydrocarbon reservoir quality. An empirical relationship between P-impedance and porosity is observed (88%) within the B-sand of Lower Goru, indicating an estimated porosity range of 15–20%. The porosity values derived from MBI at the well locations are in good correspondence with the predicted porosity of 17–18% obtained through multi-attribute analysis and probabilistic neural network (PNN), exhibiting a correlation coefficient of 97.5%. The inversion results are presented in the form of both lateral and vertical high-resolution data. Hence, this study clarifies that the model-based inversion technique can be effectively employed to estimate porosity in B-sand and can also be applied globally under similar geological conditions in challenging reservoirs, thereby aiding in reservoir characterization to reduce future exploration risk.