Time-lapse joint inversion of crosswell DC resistivity and seismic data: A numerical investigation

Abstract

Time-lapse joint inversion of geophysical data is required to image the evolution of oil reservoirs during production and enhanced oil recovery, [Formula: see text] sequestration, geothermal fields during production, and to monitor the evolution of contaminant plumes. Joint inversion schemes reduce space-related artifacts in filtering out noise that is spatially uncorrelated, and time-lapse inversion algorithms reduce time-related artifacts in filtering out noise that is uncorrelated over time. There are several approaches that are possible to perform the joint inverse problem. In this work, we investigate the structural crossgradient (SCG) joint inversion approach and the crosspetrophysical (CP) approach, which are appropriate for time-lapse problems. In the first case, the inversion scheme looks for models with structural similarities. In the second case, we use a direct relationship between the geophysical parameters. Time-lapse inversion is performed with an actively time-constrained (ATC) approach. In this approach, the subsurface is defined as a space-time model. All the snapshots are inverted together assuming a regularization of the sequence of snapshots over time. First, we showed the advantage of combining the SCG or CP inversion approaches and the ATC inversion by using a synthetic problem corresponding to crosshole seismic and DC-resistivity data and piecewise constant resistivity and seismic velocity distributions. We also showed that the combined SCG/ATC approach reduces the presence of artifacts with respect to individual inversion of the resistivity and seismic data sets, as well as with respect to the joint inversion of both data sets at each time step. We also performed a synthetic study using a secondary oil recovery problem. The combined CP/ATC approach was successful in retrieving the position of the oil/water encroachment front.

Geoelectrical monitoring: an innovative method to supplement landslide surveillance and early warning

“…), and joint inversion of cross well DC resistivity and seismic monitoring data (Karaoulis et al . ).…”

“…During the past 2 years, several studies have adopted the 3D spatial coordinate for the spatial dimension of the 4D inversion: for example, in the interpretation of complex resistivity (CR) monitoring data (Karaoulis et al 2011b), for the monitoring of a simulated leak from an underground storage tank (Rucker, Finka and Loke 2011), and for the recovering of temporal changes of the subsurface resistivity due to rainwater infiltration as well as due to the migration of sodium cyanide solution (Loke, Dahlin and Rucker 2013). The 4D inversion concept has been extended to other material properties, such as CR monitoring (Karaoulis et al 2011b), and joint inversion of cross well DC resistivity and seismic monitoring data (Karaoulis et al 2012).…”

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

“…Various spatiotemporal regularization approaches for the electric resistivity problem have been proposed in the literature. For instance, Johnson et al (2010) discuss various spatial regularizers, whereas other authors (LaBrecque and Yang, 2001;Miller et al, 2008;Kim et al, 2009;Karaoulis et al, 2012) discuss various strategies to perform time-lapse inversion including the development of regularization strategies with respect to time as done for space. Pollock and Cirpka (2012) propose a fully coupled approach of time-lapse resistivity for salt tracer tests applications in hydrogeophysics.…”

“…Various structural approaches have been developed for the joint inversion of geophysical data (e.g., Haber and Oldenburg, 1997;Zhang and Morgan, 1997). For instance, one of these approaches, called the cross-gradient method, is introduced by Gallardo and Meju (2003, 2007 and generalized to timelapse problems by Karaoulis et al (2012). In this approach, the crossproduct of the gradients in the distribution of the model parameters (for instance, the crossproduct between the gradients of density and resistivity) is minimized to obtain better tomograms reinforcing structural discontinuities.…”

Weighted cross-gradient function for joint inversion with the application to regional 3-D gravity and magnetic anomalies

“…More recently, Shi et al (2017) correlated conductivity and slowness using a cross-gradient term in the inversion of ERT and seismic refraction data. The crossgradient function has also been extended to time-lapse joint inversion for cross-hole ERT and GPR traveltime data by Doetsch & Binley (2010) and later by Karaoulis et al (2012) for cross-hole ERT and seismic data.…”