Jalal et al. /Int.J.Econ.Environ.Geol.Vol. 12(2) 00, 2021 00 c Estimation of Reservoir Porosity Using Seismic Post-Stack Inversion in Lower Indus Basin, Pakistan Sadiq Jalal 1 *, Hamza Rehman 1 , Shams-ul-Alam 2 , Abdul Wahid 1 1 Department of Earth Sciences, Quaid-i-Azam University, Islamabad, Pakistan 2 Oil and Gas Development Company Limited (OGDCL), Islamabad, Pakistan *Email: sadiqjalal786@gmail.com Received: 09 November, 2020 Accepted: 13 March, 2021 Abstract: Seismic post-stack inversion is one of the robust techniques for effective reservoir characterization. This study intends to articulate the application of Model-Based Inversion (MBI) and Probabilistic Neural Network (PNN) for the identification of reservoir properties i.e. porosity estimation. MBI technique is applied to observe the low impedance zone at the porous reservoir formation. PNN is a geostatistical technique that transforms the impedance volume into porosity volume. Inverted porosity is estimated to observe the spatial distribution of porosity in the Lower Goru sand reservoir beyond the good coverage. The result of inverted porosity is compared with that of well-computed porosity. The estimated inverted porosity ranges from 13-13.5% which shows a correlation of 99.63% with the computed porosity of the Rehmat-02 well. The observed low impedance and high porosity cube at the targeted horizon suggest that it could be a probable potential sand channel. Furthermore, the results of seismic post-stack inversion and geostatistical analysis indicate a very good agreement with each other. Hence, the seismic post-stack inversion technique can effectively be applied to estimate the reservoir properties for further prospective zones identification, volumetric estimation and future exploration. Keywords: Probabilistic neural network, model-based inversion, geo-statistical, effective porosity, impedance. Introduction The application of seismic post-stack inversion is globally practised to delineate the reservoir properties. This technique mitigates the risk, uncertainty and cost of reservoir exploration for optimum resource exploitation (Yilmaz, 2001). Post-stack inversion transforms the seismic data into inverted impedance through the integration of seismic and well log data. Inverted impedance is further transformed into petrophysical properties i-e porosity, water saturation etc., using the PNN approach (Chen and Sidney, 1997). Production of hydrocarbon in a reservoir can be enhanced if the reservoir has sufficient permeability. The higher the permeability, the higher will be the effective porosity of the reservoir (Zhao et al., 2013). Model-based inversion technique is applied to estimate inverted impedance. There is an inverse relationship between inverted impedance and porosity. PNN approach is applied to transform impedance into porosity to extract the non-linear trend. Porosity is estimated through both the methods; petrophysics and PNN technique. Moreover, estimated porosity is almost identical to well-based porosity which ranges from 13-14% with a correlation coefficient of about 99%. The resultant low impedance and high porosity zone demarcate a probable potential sand channel. This study intends to estimate the reservoir properties through the integration of model-based inversion and geostatistical analysis for identification of hydrocarbon zone at the targeted depth of reservoir (B-sand of Lower Goru formation) in the area of Mubarak field, Lower Indus Basin, Pakistan. Materials and Methods The seismic data provided by Directorate General Petroleum Concession (DGPC) consists of a 3D seismic cube and a Rehmat-02 well. The seismic data further comprises of navigation file and SEG-Y, whereas, the well data consists of various log curves among which Sonic, density and neutron logs are used specifically for well-based porosity estimation. The seismic data in the time domain is interpreted using a synthetic seismogram and converted into a depth section using a single velocity function from a time- depth chart. Moreover, post-stack inversion transforms the seismic data into inverted impedance which is further converted into petrophysical properties e.g. porosity, using the geostatistical technique. Results and Discussion To observe the regional distribution of structural geometry, seismic data is interpreted. Various faults are marked and horizons are picked with the help of a synthetic seismogram generated at Rehmat-02 well as shown in figure 1. Time grid is computed for B-sand which is multiplied with a single velocity function of 3011 m/s calculated from the time-depth chart. Therefore, a product of the time grid and velocity function gives the outcome of interpretation in the form of a depth contour map which shows that the depth varies from 3263 to 3310 meters. The contour map shows that the well is drilled on the shallower horst block of the structure as shown in figure 2. Open Access ISSN: 2223-957X Int. J. Econ. Environ. Geol. Vol. 12 (2) 00-00, 2021 Journal home page: www.econ-environ-geol.org Copyright © SEGMITE