Abstract:
The Mehar gas field lies in the lower Indus basin is a newly developed gas field.
The goal of the study was to determine the subsurface structure and characterize the
reservoir potential of the Ranikot Formation and Pab Sandstone, which are main
producing reservoirs of Mehar area. Seismic interpretation of 3D seismic cube data is
used for delineation of subsurface structure which show the existence of a huge,
bounded anticline and north to south trending thrust fault. Petrophysical study is made
on Mehar well to know reservoir properties of Ranikot and Pab sandstone formations.
Seismic inversion is a technique for making a connection between seismic data and
interpretative elastic physical characteristics of potential reservoirs. Post-stack seismic
inversion is used to estimate reservoir characteristics like as porosity and acoustic
impedance in the calculation of reservoir characterization. The reservoir parameters, as
well as fundamental variables such as acoustic impedance and porosity of the target
zone, are delineated using post-stack time migrated seismic data (POSTM) and log data
in this study. Seismic inversion and geostatistical methods were employed to finish this
task. The method for inverting seismic data into acoustic impedance is essential to the
study's main findings. Furthermore, a good wavelet representative of the given
conditions is necessary for a positive outcome. Then, applying probabilistic neural
network (PNN) techniques, geostatistical inversion is used to estimate the porosity,
volume of shale and water saturation of Ranikot and Pab sandstone using well Mehar-
01,02 and 03. The impedance volume is converted to volume of shale, porosity and
water saturation volume using PNN, and the results are compared using the
petrophysical parameters of wells. Bayesian classification is run in 3D data cube to
predict the lithologies and fluid present in the reservoir. For Bayesian classification two
type of cross plot are used for prediction of lithology and fluid, P-impedance vs Vp/Vs
for fluid prediction and Lambda-Rho vs Vp/Vs for lithology prediction with density
plotted on Z axis. The cross-plots clearly separated and delineated the lithofluid classes
(wet sand, gas sand, shale, and limestone) with specific orientation/patterns.
