Experience on controlled-source electromagnetic performance for exploration in Norway

Berre, Lodve; Morten, Jan Petter; Baillie, Graeme; Nerland, Elias A.

doi:10.1190/int-2019-0307.1

Cited by 6 publications

(8 citation statements)

References 11 publications

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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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“…Methods were originally developed for land application, but only used in limited cases. The success in the marine EM market proofed the technology value and its commercial viability [8].…”

mentioning

confidence: 99%

“…Electromagnetic monitoring technology is now well proven [5][6][7][8] for hydrocarbon applications while in the 1980s was still in the research phase [6]. The equipment is fully commercial, software algorithms are well tested, and surface measurements confirm in many cases borehole measurements.…”

mentioning

confidence: 99%

Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition

Strack¹,

Davydycheva²,

Passalacqua³

et al. 2021

Preprint

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One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

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“…Electromagnetic monitoring technology is now well proven [5][6][7][8] for hydrocarbon applications, while in the 1980s it was still in the research phase [6]. The equipment is fully commercial, software algorithms are well tested, and surface measurements confirm in many cases' borehole measurements.…”

mentioning

confidence: 99%

“…Methods were originally developed for land application, but only used in limited cases. The success in the marine EM market proofed the technology value and its commercial viability [8]. While the methodology, instruments, and interpretation methods are mature, they are not commercial enough to let the business drive the technology.…”

mentioning

confidence: 99%

Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition

Strack¹,

Davydycheva²,

Passalacqua³

et al. 2021

JMSE

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Fluid imaging is one of the key geophysical technologies for the energy industry during energy transition to zero footprint. We propose better Cloud-based fluid distribution imaging to allow better, more optimized production, thus reducing carbon dioxide (CO2) footprint per barrel produced. For CO2 storage, the location knowledge of the stored fluids is mandatory. Electromagnetics is the preferred way to image reservoir fluids due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to cost optimization of field operations. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and even, for the interpretation, to close to 24 h. This then opens new doors for the breakthrough of this technology from exploration to production and monitoring. It allows the application envelope to be enlarged to much noisier environments where real time acquisition can be optimized based on the acquired data. Once all components are commercialized, the full implementation could become a real game changer by providing near real time 3-dimensional subsurface images in support of the energy transition.

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Experience on controlled-source electromagnetic performance for exploration in Norway

Cited by 6 publications

References 11 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

Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition

Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition

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