The lower Indus basin is one of the largest hydrocarbon producing sedimentary basins in Pakistan. It is characterized by the presence of many hydrocarbon-bearing fields including clastic and carbonates proven reservoirs from the Cretaceous to the Eocene age. This study has been carried out in the Sanghar oil field to evaluate the hydrocarbon prospects of basal sand zone of lower Goru Formation of Cretaceous by using complete suite of geophysical logs of different wells. The analytical formation evaluation by using petrophysical studies and neutron-density crossplots unveils that litho-facies mainly comprising of sandstone. The hydrocarbons potentialities of the formation zone have been characterized through various isoparameteric maps such as gross reservoir and net pay thickness, net-to-gross ratio, total and effective porosity, shaliness, and water and hydrocarbons saturation. The evaluated petrophysical studies show that the reservoir has net pay zone of thickness range 5 to 10 m, net-to-gross ratio range of 0.17 to 0.75, effective porosity range of 07 to 12 %, shaliness range of 27 to 40 % and hydrocarbon saturation range of 12 to 31 %. However, in the net pay zone hydrocarbon saturation reaches up to 95%. The isoparametric charts of petrophysically derived parameters reveal the aerial distribution of hydrocarbons accumulation in basal sand unit of the lower Goru Formation which may be helpful for further exploration.

We develop a novel elastic amplitude-variation-with-offset (AVO) inversion process to estimate the fluid saturation and effective pressure or variations in these properties from time-lapse seismic data sets. These changes occur in oil and gas reservoirs caused by fluid injection or hydrocarbon production that leads to changes in the elastic wave properties, reflectivity, and seismic response. Our method is based on a seismic forward model that consists of a linearized AVO equation and a rock-physics model. The AVO equation links the elastic wave properties to seismic reflection amplitudes, whereas the rock-physics model maps the saturation and pressure into seismic velocities and density. The inversion approach relies on the gradient descent technique to estimate the unknown variables by searching for the minimum of the least-squares misfit between the observed and modeled data. The first-order gradient equations of the least-squares data misfit function with respect to effective pressure and water saturation are derived by using the adjoint-state method and the chain rule. The optimization method used to minimize the misfit function and obtain the best optimal solution is a limited-memory quasi-Newton algorithm. This inversion process allows us to incorporate prior constraints by using the logistic function to map the model variables to a bounded range. To achieve a stable solution to ill-posed inversion problems, optimal regularization weights are applied. The application of the developed workflow on 1D synthetic and real well-log data from the Edvard Grieg oil field simulating various saturation-pressure conditions during production demonstrates the validity of the approach with different noise levels. The inversion is then applied to a 2D synthetic data set modeled from the reservoir model of the Smeaheia field, a potential site for a large-scale offshore CO2 storage field located in the North Sea. Our results illustrate that our inversion method efficiently and accurately estimates reservoir saturation and pressure variations.

SUMMARY: Seismic inversion of amplitude versus offset (AVO) data in viscoelastic media can potentially provide high-resolution subsurface models of seismic velocities and attenuation from offset/angle seismic gathers. P- and S-wave quality factors (Q), whose inverse represent a measure of attenuation, depend on reservoir rock and pore fluid properties, in particular, saturation, permeability, porosity, fluid viscosity and lithology; however, these quality factors are rarely taken into account in seismic AVO inversion. For this reason, in this work, we aim to integrate quality factors derived from physically based models in AVO inversion by proposing a gradient descent optimization-based inversion technique to predict the unknown model properties (P- and S-wave velocities, the related quality factors and density). The proposed inversion minimizes the non-linear least-squares misfit with the observed data. The optimal solution is iteratively obtained by optimizing the data misfit using a second-order limited-memory quasi-Newton technique. The forward model is performed in the frequency–frequency-angle domain based on a convolution of broad-band signals and a linearized viscoelastic frequency-dependent AVO (FAVO) equation. The optimization includes the adjoint-state-based gradients with the Lagrangian formulation to improve the efficiency of the non-linear seismic FAVO inversion process. The inversion is tested on synthetic seismic data, in 1-D and 2-D, with and without noise. The sensitivity for seismic quality factors is evaluated using various rock physics models for seismic attenuation and quality factors. The results demonstrate that the proposed inversion method reliably retrieves the unknown elastic and an-elastic properties with good convergence and accuracy. The stability of the inverse solution especially seismic quality factors estimation relies on the noise level of the seismic data. We further investigate the uncertainty of the solution as a function of the variability of the initial models.