CSEM Pre-well Predictions in the Johan Castberg Area, Barents Sea

Løseth, L. O.; Hansen, Jan Ove; Wiik, Torgeir; Roudot, M.; Dubois, B.; Nguyen, Anh Kiet; Henriksen, Leif Bjørnar

doi:10.3997/2214-4609.201413218

Cited by 3 publications

(2 citation statements)

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“…In geological settings of moderate complexity and in the absence of non‐hydrocarbon‐related resistors (so called antimodels), large shallow hydrocarbon accumulations are easily detected and mapped by inverting CSEM data, even if image resolution and vertical positioning accuracy are limited. CSEM has for instance a good track record for exploration and de‐risking of shallow prospects in the Norwegian Barents Sea, where seismic often cannot discriminate between non‐commercial gas accumulations and profitable hydrocarbon‐filled traps (Berre et al., 2020; Fanavoll et al., 2014; Gabrielsen et al., 2013; Granli et al., 2017; Løseth et al., 2014; Løseth et al., 2015; Nordskag et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

Causse

2023

Geophysical Prospecting

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Non‐linear inversion of controlled source electromagnetic data is non‐unique. Inversion ambiguity and uncertainty grow with model complexity and limitations in sensitivity, for example when imaging deep targets. Uncertainties should be estimated. The best way to do that is by statistical inversion techniques. Because of the large number of parameters of 3D models, these methods are too costly for 3D applications. But neglecting or underestimating uncertainties often leads to erroneous interpretation and poor exploration decisions. The number of inversion parameters can be dramatically reduced while still being able to describe a complex 3D subsurface. To obtain that, I represent prior information by an input ensemble of geologically reasonable prior models. The model space is defined by the principal directions of this ensemble, where the inversion parameters are the coefficients of the different components. In this model space, inversion becomes much more efficient and can produce an updated ensemble of posterior models at the same computational cost as a controlled‐source electromagnetic inversion project using conventional non‐linear inversion techniques. I present examples where unique Gauss–Newton inversions lead to difficult interpretations and poor conclusions about hydrocarbon presence and saturation. The new approach provides a quantitative estimation of resistivity ambiguities and uncertainties leading to better interpretation and safer decisions. The examples use 2D synthetic controlled‐source electromagnetic data and 3D controlled‐source electromagnetic field data.

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Section: Introductionmentioning

confidence: 99%

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

Causse

2023

Geophysical Prospecting

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“…CSEM is a method for remotely measuring the resistivity of the subsurface (Cox, 1980;Chave and Cox, 1982). Its sensitivity towards thin resistive layers has sparked a large interest in the hydrocarbon exploration and exploitation industry (Eidesmo et al, 2002;Ellingsrud et al, 2002;Constable, 2010;Løseth et al, 2015). CSEM data are typically inverted into resistivity models for interpretation and integration with other geophysical data.…”

Section: Introductionmentioning

confidence: 99%

Comparing large-scale 3D Gauss–Newton and BFGS CSEM inversions

Nguyen¹,

Nordskag²,

Wiik³

et al. 2016

SEG Technical Program Expanded Abstracts 2016

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Electromagnetic signals are exponentially attenuated in conductive media. Thus, marine controlled-source electromagnetic (CSEM) data where the source and the receivers are located in the water column has exponentially low sensitivity towards the deep stratigraphy, compared to the shallow stratigraphy. In addition, CSEM inversions are also highly non-linear and ill-posed. It is therefore often difficult to achieve good inversion results for the deeper part of the subsurface using gradient based inversion methods. In this abstract, we describe a large-scale 3-dimensional anisotropic Gauss-Newton (3DGN) CSEM inversion implementation and discuss its advantages over gradient based algorithms. We also show, by synthetic and real data case studies, the large improvements in the 3DGN inversion results compared to those from the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm.

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Distorted Born iterative T-matrix method for inversion of CSEM data in anisotropic media

Jakobsen

Tveit

2018

Geophysical Journal International

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CSEM Pre-well Predictions in the Johan Castberg Area, Barents Sea

Cited by 3 publications

References 0 publications

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

Comparing large-scale 3D Gauss–Newton and BFGS CSEM inversions

Distorted Born iterative T-matrix method for inversion of CSEM data in anisotropic media

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