Search

The combination of geophysical surface-based imaging techniques, including seismic and electrical resistivity tomography (ERT), is now common practice to obtain a more accurate characterization of subsurface structures. Due to model non-uniqueness and geological heterogeneity, conventional travel-time tomography cannot solely reveal hidden layers (i.e., low-velocity zones embedded between layers of higher velocities) in the subsurface. Hence, we present a joint inversion algorithm based on a normalized cross-gradients function to detect hidden low-velocity layers. The structural similarity between P-wave velocity (V-p) and resistivity fields is enhanced by incorporating the normalized cross-gradients constraint in the joint inversion algorithm. Improved structural similarity can mitigate the problem of the recovery of a hidden layer. We also take advantage of a priori information derived from borehole geological data to reduce the continuous range of possible solutions (i.e., exact-data non-uniqueness). In both joint and separate inversions, an auxiliary damping factor is used to ensure convergence, and also the smoothness constraints are applied to deal with instability stemming from error in the data. To verify the performance of the joint inversion procedure, the algorithm is tested on synthetic and real data examples with emphasis on hidden low-velocity layer detection. Numerical experiments demonstrate that the joint inversion strategy can produce more reliable and better velocity models of the subsurface structures as compared with those obtained through individual inversions. We conclude that this simultaneous joint inversion of V-p and ERT integrates the best of both schemes and makes it possible to improve resolution, and, hence, reduces uncertainties in hidden low-velocity layer problems.

Diffraction arrivals are important data that have increasingly been used to delineate the sources of diffractors and to explore subsurface discontinuities. In prestack data, diffractions are both zero- and non-zero offset hyperbolas while reflections are only zero-offset hyperbolas. An iterative algorithm using an apex-shifted Radon transform (ASRT) approach is presented in this paper that uses the diffraction hyperbolic trajectory similar to that of prestack time migration in order to locate diffractors and to estimate their corresponding background velocities. Because diffraction energy is generally weak in seismic data and particularly in prestack data, noise attenuation and edge enhancement methods are applied on the instantaneous phase of the seismic data instead of the amplitude data. This means that the phase data are input to the ASRT algorithm. The method is then tested on two synthetic datasets (a point-diffraction model with randomly distributed diffractors and the 2D BP/SEG salt model) and one real data example. Results show that this method can locate the diffractors reasonably well on the rough surfaces of the salt dome and the discontinuities associated with structures such as paleo-channels and faults. Our analysis of the estimated velocities suggests that they are generally valid for diffraction delineation; however, the accuracy of the estimation decreases as background velocity and depth increase.

In this study, a carbonate field from Iran was studied. Estimation of rock properties such as porosity and permeability is much more challenging in carbonate rocks than sandstone rocks because of their strong heterogeneity. The frame flexibility factor (gamma) is a rock physics parameter which is related not only to pore structure variation but also to solid/pore connectivity and rock texture in carbonate reservoirs. We used porosity, frame flexibility factor and bulk modulus of fluid as the proper parameters to study this gas carbonate reservoir. According to rock physics parameters, three facies were defined: favourable and unfavourable facies and then a transition facies located between these two end members. To capture both the inversion solution and associated uncertainty, a complete implementation of the Bayesian inversion of the facies from pre-stack seismic data was applied to well data and validated with data from another well. Finally, this method was applied on a 2D seismic section and, in addition to inversion of petrophysical parameters, the high probability distribution of favorable facies was also obtained.