3D CSEM modeling and inversion in complex geologic settings

Plessix, René‐Edouard; Sman, P. van der

doi:10.1190/1.2792489

Cited by 18 publications

(5 citation statements)

References 6 publications

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“…Large-scale 3D imaging is also receiving considerable attention in the interpretation of CSEM data, , Carazzone et al 2008, Gribenko and Zhdanov 2008, Plessix and Mulder 2008, Plessix and van der Sman, 2007, Zach et al 2008. While one-dimensional (1D) modeling and inversion is relatively easy and trial and error 3D forward modeling seemingly straight forward (Hoversten et al 2006;4 Weiss & Constable 2006, Green et al, 2005, the need for 3D imaging is necessary as the search for hydrocarbons now increasingly occurs in highly complex situations where hydrocarbon effects are subtle aspects of the total offshore geological environment.…”

Section: Introductionmentioning

confidence: 99%

Imaging CSEM data in the presence of electrical anisotropy

2010

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Formation anisotropy should be incorporated into the analysis of controlled source electromagnetic (CSEM) data because failure to do so can produce serious artifacts in the resulting resistivity images for certain data configurations of interest. This finding is demonstrated in model and case studies. Sensitivity to horizontal resistivity will be strongest in the broadside electric field data where detectors are offset from the tow line. Sensitivity to the vertical resistivity is strongest for over flight data where the transmitting antenna passes directly over the detecting antenna. Consequently, consistent treatment of both over flight and broadside electric field measurements requires an anisotropic modeling assumption. To produce a consistent resistivity model for such data we develop and employ a 3D CSEM imaging algorithm that treats transverse anisotropy. The algorithm is based upon non-linear conjugate gradients and full wave equation modeling. It exploits parallel computing systems to effectively treat 3D imaging problems and CSEM data volumes of industrial size. Here we use it to demonstrate the anisotropic imaging process on model and field data sets from the North Sea and offshore Brazil.We also verify that isotropic imaging of over flight data alone produces an image generally consistent with the vertical resistivity. However, superior data fits are obtained when the same over flight data are analyzed assuming an anisotropic resistivity model. 2

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Section: Introductionmentioning

confidence: 99%

Imaging CSEM data in the presence of electrical anisotropy

2010

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“…From numerical tests, the offset data weighting scheme with α < 0.5 produces the best results. Similar schemes can also be found in Plessix and van der Sman (2007).…”

Section: Formulationsmentioning

confidence: 61%

“…A more rigorous approach to address this type of application is the full nonlinear inversion approach, for example see Gribenko and Zhdanov (2007), Plessix and van der Sman (2007) and Abubakar et al (2006, 2008). In such an approach the investigation domain is subdivided into pixels and by using an optimization process the location, shape and conductivity of the reservoir are reconstructed.…”

Section: Introductionmentioning

confidence: 99%

Inversion of controlled‐source electromagnetic data using a model‐based approach

Abubakar

Habashy

et al. 2010

Geophysical Prospecting

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A B S T R A C TA two-and-half dimensional model-based inversion algorithm for the reconstruction of geometry and conductivity of unknown regions using marine controlled-source electromagnetic (CSEM) data is presented. In the model-based inversion, the inversion domain is described by the so-called regional conductivity model and both geometry and material parameters associated with this model are reconstructed in the inversion process. This method has the advantage of using a priori information such as the background conductivity distribution, structural information extracted from seismic and/or gravity measurements, and/or inversion results a priori derived from a pixel-based inversion method. By incorporating this a priori information, the number of unknown parameters to be retrieved becomes significantly reduced. The inversion method is the regularized Gauss-Newton minimization scheme. The robustness of the inversion is enhanced by adopting nonlinear constraints and applying a quadratic line search algorithm to the optimization process. We also introduce the adjoint formulation to calculate the Jacobian matrix with respect to the geometrical parameters. The model-based inversion method is validated by using several numerical examples including the inversion of the Troll field data. These results show that the modelbased inversion method can quantitatively reconstruct the shapes and conductivities of reservoirs.

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“…Furthermore, based on the measured ELF field distributions, the inversion method for the conductivity and other parameters is also carried out. The available numerical methods for marine exploration can be found in the references (Gribenko and Zhdanov 2007;Plessix and van der Sman 2007;Zach et al 2008aZach et al , 2008b.…”

Section: Elf Wave Propagation Along the Ocean Floor And The Marine Comentioning

confidence: 99%

Historical and Technical Overview of SLF/ELF Electromagnetic Wave Propagation

Pan¹,

Li²

2013

Advanced Topics in Science and Technology in China

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In general, very low frequency (VLF) refers to electromagnetic waves with frequencies from 3 kHz to 30 kHz; ultra low frequency (ULF) refers to frequency range between 300 Hz to 3 kHz; the frequency range of 30 Hz to 300 Hz is defined as super low frequency (SLF); and the frequency below 30 Hz is referred to as extremely low frequency (ELF). In some early studies, alternative definitions may have been used with frequency range of 3 Hz to 3 kHz generally referred to ELF. In this book, the emphasis is on SLF (30-300 Hz) and ELF (below 30 Hz) ranges, using the MKS system of units and the time dependency e −iωt . Medium Characteristics of SLF/ELF Wave PropagationDue to the long wavelength, SLF/ELF waves, when excited and propagated on and near the Earth's surface, will cover lithosphere, atmosphere and ionosphere, whose electromagnetic characteristics differ significantly in their propagation paths. The permeability of the atmosphere and ionosphere is approximately μ 0 , the permeability in free space, as well as that of the lithosphere, except in the regions that are rich in iron, nickel, cobalt, etc. Therefore, the permeability of the lithosphere can be also treated as μ 0 , if neither the transmitter nor the receiver is located in mineral-rich areas. The atmosphere is a non-conductive medium with negligible conductivity, whose permittivity is close to ε 0 , the permittivity of free space. While the lithosphere is conductive, the conductivity of sea water σ sea is in the range of 2.5-5.5 S/m, and the conductivity σ g of rock and soil in the range of 10 −2 -10 −4 S/m. The dielectric constant ε r of the lithosphere does not have large effects on the propagation of SLF/ELF waves.The ionosphere is composed of partially ionized gas, which includes electrons, ions, and electrically neutral particles, above 70 km or so from the sea level. The electrons and ions make the ionosphere conductive, thus the majority of the energy of the incident VLF/SLF/ELF waves will be reflected by the ionosphere. When both the transmitter and the receiver are located in the space between the ground and the W. Pan, K. Li, Propagation of SLF/ELF Electromagnetic Waves,

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3D CSEM modeling and inversion in complex geologic settings

Cited by 18 publications

References 6 publications

Imaging CSEM data in the presence of electrical anisotropy

Imaging CSEM data in the presence of electrical anisotropy

Inversion of controlled‐source electromagnetic data using a model‐based approach

Historical and Technical Overview of SLF/ELF Electromagnetic Wave Propagation

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