Depth domain inversion to improve the fidelity of subsalt imaging: a Gulf of Mexico case study

Accurate geophysical reservoir characterisation in complex geologic environments remains a challenge. In particular, conventional methods of amplitude inversion assume that amplitudes in the seismic image are correctly located and can be inverted to elastic parameters from which a true representation of rock proper¬ties can be derived. However, complex geology, often com¬bined with limitations imposed by surface seismic acquisition geometries, can lead to inadequate illumination of subsurface targets, which can have detrimental effects on the amplitudes and phase of the migrated image. Conventional amplitude inversion techniques do not compensate for these amplitude and phase variations. Consequently, imprints of various non-geological effects and a complex overburden will manifest themselves in the results of seismic inversion, leading to less reliable estimation of acoustic and elastic parameters. An additional challenge to accurate amplitude inversion in complex geologic environments is that depth imaging is normally required to obtain a reliable image of the subsurface, while current amplitude inversion techniques are usually implemented in the time domain. This difference in approach between the imaging and inversion steps can compromise the fidelity of the attributes derived from seismic inversion. In order to improve consistency between structural imag¬ing and rock property estimation, a technique has been developed to perform amplitude inversion directly in the depth domain. The inversion workflow uses point spread functions to capture and correct for space-, depth- and dip-dependent illumination effects resulting from the acquisition geometry and complex geology. The amplitude inversion is performed in the depth domain and the output is a reflectivity image and associated acoustic impedance volume corrected for illumina¬tion effects, thus creating more consistent and reliable imaging products and rock property attributes from depth-migrated datasets. This article presents the application of the depth domain inversion workflow to a long-offset full-azimuth (FAZ) dataset from the Green Canyon area of the Gulf of Mexico (GoM) (Letki et al., 2015).