Estimation of Petrophysical Parameters from Seismic Inversion by Combining Particle Swarm Optimization and Multilayer Linear Calculator

In heterogeneous reservoir rocks, the accurate characterization of lithology and reservoir parameters is significant to minimize drilling risks and to improve oil and gas recoveries. In this work, a joint inversion strategy based on multilayer linear calculator (MLC) and particle swarm optimization (PSO) algorithm was applied to predict the spatial variations of key petrophysical (porosity, permeability, and saturation) and geomechanical parameters (Young's modulus, Poisson's ratio, and brittleness) for inter-well regions. In this method, acoustic impedance (AI) models are computed from post-stack seismic amplitude data by applying the proposed strategy (MLC + PSO) and back propagation neural network-based seismic inversion in the time domain with measured log density and velocity as constraints. The obtained results reveal that the proposed strategy, which combines MLC and PSO, leads to the optimization of lateral and vertical facies heterogeneities and accurate prediction of reservoir parameter distribution, i.e., the low AI is related to sand facies and corresponds to high porosity, permeability, saturation, and mid-range of Young's modulus. The time slice maps of inverted porosity and permeability at various time intervals indicate a reasonable calibration with the measured core and well log data. The methodology proposed in this study may be considered useful for other basins in Pakistan with similar geological settings and anywhere in the world for reservoir characterization, particularly for intercalated shale and variable depositional environments.