Quantifying thin heterogeneous gas sand facies of Rehmat gas field by developing petro elastic relationship in fine stratigraphic layers through bayesian stochastic seismic inversion

The Lower Goru Formation (LGF) sand intervals are prolific producers in the Central Indus Basin (CIB), Pakistan. The B-Interval of LGF is a proven reservoir of the Rehmat gas field, having heterogeneous and thin sand layers (14–17 m) below seismic tuning thickness. To quantify these potential layers, a petro-elastic relationship is developed using seismic and well data. Well data although having high resolution, lacks in many aspects, such as poor density logs due to bad boreholes along with the missing shear sonic (Vs), adding more challenges to characterize a complex reservoir. Therefore, an extended cemented sandstone model is established by incorporating the petro-elastic models (PEMs) of the reservoir's litho-fluid facies in the rock physics modeling (RPM) framework. RPM compensated for the lithological variability, quartz cementation impact, missing and bad density logs. The missing low and high frequencies of band-limited seismic data are retrieved through constraining wells by geostatistical parameters for enhanced visualization of thin-bedded heterogeneous sand. Bayesian stochastic seismic inversion (BSSI) addressed reservoir challenges by incorporating the PEMs responses along with consigning the frequencies limitation, through a low-frequency model and variograms from the sparse well data for high frequencies. For optimized reservoir characterization, a 3D seismic dataset is inverted in a fine-scale stratigraphic grid of 0.1 ms interval yielding high-resolution elastic properties. These enhanced elastic attributes are integrated with petrophysical relationships simulated through probability density functions that permit elastic attributes to be translated into reservoir characteristics (geological facies). So, an integrated technique of rock physics modeled logs with BSSI successfully identified thin heterogeneous gas sands through several high-resolution realizations with associated uncertainty. •Reservoir characterization by integrating petrophysics, rock physics & Bayesian stochastic seismic inversion.•Petro-elastic relationship via advance rock physics modeling with optimization and prediction of logs.•The high-resolution elastic properties populated via variogram and simulated using the MCMC..•Thin gas sand facies classified using the Kernel PDF's and estimated after results validation.•Heterogeneous, below seismic resolution, gas sands successfully highlighted with delineation of new prospects.