2D joint inversion of geophysical data using petrophysical clustering and facies deformation

Abstract

Geologic expertise and petrophysical relationships can be brought together to provide prior information while inverting multiple geophysical data sets. The merging of such information can result in more realistic solution in the distribution of the model parameters. We have evaluated the geophysical inverse problem in terms of Gaussian random fields with mean functions controlled by petrophysical relationships and covariance functions controlled by a prior geologic cross section, including the definition of spatial boundaries for the geologic facies. The petrophysical relationship problem is formulated as a regression problem upon each facies. The inversion of the geophysical data is performed in a Bayesian framework. We have developed the usefulness of this strategy using a first synthetic case for which we have performed a joint inversion of gravity and galvanometric resistivity data with the stations located at the ground surface. The joint inversion was used to recover the density and resistivity distributions of the subsurface. In a second step, we considered the possibility that the facies boundaries were deformable and their shapes were inverted as well. We used the level-set approach to perform such deformation preserving a priori topological properties of the facies throughout the inversion. With the help of a priori facies petrophysical relationships and the topological characteristics of each facies, we made a posteriori inference about multiple geophysical tomograms based on their corresponding geophysical data misfits. We have applied this method to a second synthetic case showing that we can recover the heterogeneities inside the facies, the mean values for the petrophysical properties, and, to some extent, the facies boundaries using the 2D joint inversion of gravity and galvanometric resistivity data.

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

“…Quantitative integration of these two disciplines is an active, yet underexplored research area. Recent research works (Revil et al, 2015, Zhou et al, 2016, Zhang and Revil, 2015 illustrate the increase of interest from the community, and show that integration of multiple datasets is a way forward in tackling the limitations of current inversion methodologies.…”

“…To mitigate the lack of quantitative integration between geology and geophysics, several authors developed geophysical inversion algorithms addressing the geometry of the inverted models. Fullagar and Pears (2007), Gallardo et al (2005), Guillen et al (2008), Wellmann et al (2013) and Zhang and Revil (2015) developed geology-geophysics inversion algorithms that allow the geometry of the geological structures to vary in order to honour geophysical data. Li et al (2010), Davis et al (2012), McMillan et al (2015 and Balidemaj and Remis (2010) parameterize geology to include model geometry in inversion.…”

Geophysical characterisation of the groundwater–surface water interface

“…Petrophysical relations can be used in joint inversions to relate two independent geophysical methods (e.g. Hoversten et al, 2006;Zhang and Revil, 2015) or after geophysical inversion to translate geophysical data. Although mechanistic models exist (e.g.…”

Integration of Borehole Data in Geophysical Inversion Using Fuzzy Clustering

“…Lelièvre et al (2009) stated an approach to include prior physical properties and structural information into deterministic inversion. Zhang and Revil (2015) used petrophysical relationships within each unit to constrain inversion of gravity and electrical resistivity data. The approach of Sun and Li (2016) is similar, they divide the inversion domain into subdomains, each area has a different clustering approach.…”