Gas-bearing sands appraisal through inverted elastic attributes assisted with PNN approximation of petrophysical properties
The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. For the optimized production, it is necessary to characterize the gas-sand facies from the rest of the wet-sands and shales of the Pab Formation. Therefore, an approach is adopted based on a relation between petrophysical and elastic properties to delineate the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties such as p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62) are determined from the simultaneous prestack seismic inversion, which leads towards the identification of gas sands in the field. Further, inverted elastic attributes along with well-based lithologies are integrated into the Bayesian framework to estimate the gas sands probabilities surrounding the drilled well locations. Bulk volumes of PHIE and clay are approximated from elastic volumes trained on well logs through Probabilistic Neural Networking (PNN), which better handle heterogeneity effects. The outcome indicated the channelized gas-sands passing through existing well locations having less clay content with maximum effective porosities of 9%, hence confirming the good quality of the reservoir. Such techniques can be broadly adopted in the producing basins to recognize economic potential zones so that maximum production can be obtained from them.