Data Preprocessing and Starting Model Preparation for 3D Inversion of Marine CSEM Surveys

Zach, J. J.; Roth, F.; Yuan, Hongtao

doi:10.3997/2214-4609.20147712

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…2) Pre-processing: data conditioning follows largely Zach et al (2008b), with the exception of the data-driven determination of the rotation angle.…”

Section: Methodsmentioning

confidence: 99%

Full‐azimuth, anisotropic 3D EM inversion applied to a low‐resistivity pay reservoir with well control

Ziolkowski

Scherrer

Pham

et al. 2010

SEG Technical Program Expanded Abstracts 2010

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We demonstrate advances in 3D EM inversion-based subsurface imaging on a challenging target in a mature area of the Gulf of Mexico. These advances include the use of the anisotropic (v , h) cube as well as a data weight scheme which permits inversion of all azimuthal data in the 3D grid. Our inversion is based on a quasi-Newton optimization algorithm with approximate calculation of the Hessian matrix and a finite-difference time-domain forward modeler. Its capability to resolve small targets of lowresistivity pay in complex geology is confirmed by the case example presented here. A proven hydrocarbon find (¡ < 5 ¢ m versus background, 2 km x 2 km) is compared with well and seismic data. There is a significant improvement in resolution over previous inversions using isotropic 3Dinversion, however, due to the acquisition in deep water and lack of substantial anisotropy, the target was identified in both cases.

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“…2) Pre-processing: data conditioning follows largely Zach et al (2008b), with the exception of the data-driven determination of the rotation angle.…”

Section: Methodsmentioning

confidence: 99%

Full‐azimuth, anisotropic 3D EM inversion applied to a low‐resistivity pay reservoir with well control

Ziolkowski

Scherrer

Pham

et al. 2010

SEG Technical Program Expanded Abstracts 2010

View full text Add to dashboard Cite

show abstract

“…DATA PREPROCESSING WORKFLOW Figure 1 shows the principal steps of the pre-processing workflow for inversion or other advanced processing in the frequency-domain, which was first completely introduced in [17]. The calibration of the data occurs in the time-domain and is dependent upon the specific receiver hardware used.…”

Section: Methodology 1: Data Conditioningmentioning

confidence: 99%

“…The standard approach is to execute plane-layer inversion of individual receivers with a robust simulated-annealing inversion scheme [17,18] and to interpolate a layered resistivity model from the resulting conductivity-depth-profiles. This is chiefly due to the large parameter space on the order of 10 7 unknowns, if we assume an inversion grid of (Δx x Δy x Δz) ~ (200 m x 200 m x 50 m).…”

Section: Starting Model Preparationmentioning

confidence: 99%

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3D Inversion-Based Interpretation of Seabed Logging Data

Zach

Frenkel

2009

All Days

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The Marine Controlled-Source Electromagnetic (CSEM) method has been evolving into a geophysical imaging tool for increasingly complex geological settings, in which multiple resistive bodies can be resolved. An advanced subsurface imaging workflow for 3D CSEM surveys is presented, which reproduces the subsurface to within a spatial resolution determined frequencies included. The performance of our advanced processing workflow is demonstrated using a case study from the Gulf of Mexico, where a dense 3D grid was acquired over an area where high-quality seismic data, as well as well log control was given.At the heart of our 3D workflow is an inversion methodology with approximate Hessian-based optimization and a fast finitedifference time-domain forward operator. The optimization matches the synthetic to the measured field within 100-200 iterations and is sufficiently robust in 3D to avoid expensive regularization schemes. The sensitivity of the gradient-based inversion to the starting model is addressed by investing considerable effort in building 1D inversion-based starting model. At the same time, 3D inversion algorithms for survey layouts including azimuthal data demand high quality data conditioning, for which we present a processing sequence from time-domain electromagnetic data acquired by seabed receivers to frequency domain data and weights for inversion.Detection and delineation of reservoirs in the presence of salt is recognized as a major challenge to CSEM methods. In order to accurately interpret 3D data in such a complex environment, the true resistivity cube is built from a sequence of constrained inversion-based interpretation steps. Using a dataset acquired in 2008 in the Gulf of Mexico, we demonstrate the ability of our 3D-technology to resolve small (2km x 2km), low resistivity pay (Δρ<5Ωm) targets with in the vicinity (< 1km) of large salt bodies. In this case study, the 3D method converged within 1 week running on 150 parallel nodes.

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“…Here η is a constant factor and F Noise κ is the noise floor representing the background signal level. The noise is estimated in a data processing step by averaging over frequency windows close to the source signal frequency (Zach et al, 2008). The noise floor estimate is additionally used to define a signal-tonoise threshold for data to be included in the inversion.…”

Section: D Inversion Data Weightsmentioning

confidence: 99%