Marine controlled-source electromagnetic (CSEM) data can be highly sensitive to the presence of resistive hydrocarbon bearing layers in the subsurface. Yet, due to the relatively poor depth resolution of CSEM data and the smoothness constraints imposed by electromagnetic (EM) inversion methods, the resulting resistivity models are often highly smoothed-out, typically underestimating the reservoir resistivity and overestimating its thickness. Conversely, seismic full-waveform inversion (FWI) can accurately recover the depths of seismic velocity changes, yet, is relatively insensitive the presence of hydrocarbons. In spite of their low depth resolution, CSEM data have been shown to be highly sensitive to the resistivity-thickness product of buried resistive layers, suggesting that if the thickness of a target layer can be constrained a priori, very accurate resistivity estimates may be obtained. We developed a method for leveraging the high depth resolution of FWI into a standard CSEM inversion algorithm so that the resulting resistivity models have depth constraints imposed by the seismic structure and consequently may obtain more accurate resistivity estimates. The seismically regularized CSEM inversion that we propose is conceptually similar to minimum-gradient support (MGS) regularization, but it uses regularization weights based on gradients in the seismic velocity model rather than the self-reinforcing model resistivity gradients used in the typical MGS scheme. A suite of synthetic model tests showed how this approach compares with standard smooth and MGS inversions for a range of rock types and hence, levels of correlation between the seismic and resistivity structures, showing that a significantly improved resistivity model can be obtained when the velocity and resistivity profiles are correlated in depth. We also found that this regularization weighting method can be extended to use depth constraints from geophysical data other than seismic velocity models. Tests on a real data example from the Pluto gas field demonstrated how the regularization weights can also be set using a nearby well log, resulting in a more compact estimate of the reservoir resistivity than possible with a standard smooth inversion.

A combination of 1D and 3D forward and inverse solutions is used to quantify the sensitivity and resolution of conventional controlled source electromagnetic (CSEM) data collected using a horizontal electric dipole source to transverse electrical anisotropy located in a deep-water exploration reservoir target. Since strongly anisotropic shale layers have a vertical resistivity that can be comparable to many reservoirs, we examine how CSEM can discriminate confounding shale layers through their characteristically lower horizontal resistivity. Forward modeling demonstrates that the sensitivity to reservoir level anisotropy is very low compared to the sensitivity to isotropic reservoirs, especially when the reservoir is deeper than about 2 km below the seabed. However, for 1D models where the number of inversion parameters can be fixed to be only a few layers, both vertical and horizontal resistivity of the reservoir can be well resolved using a stochastic inversion. We find that the resolution of horizontal resistivity increases as the horizontal resistivity decreases. We show that this effect is explained by the presence of strong horizontal current density in anisotropic layers with low horizontal resistivity. Conversely, when the reservoir has a vertical to horizontal resistivity ratio of about 10 or less, the current density is vertically polarized and hence has little sensitivity to the horizontal resistivity. Resistivity anisotropy estimates from 3D inversion for 3D targets suggest that resolution of reservoir level anisotropy for 3D targets will require good a priori knowledge of the background sediment conductivity and structural boundaries.3