The Potential of the Controlled Source Electromagnetic Method: A Powerful Tool for Hydrocarbon Exploration, Appraisal, and Reservoir Characterization

Ziolkowski, Anton; Wright, David

doi:10.1109/msp.2012.2192529

Cited by 9 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimated noise in Figure 17d is rather low frequency. One candidate for this is magnetotelluric noise which increases dramatically at low frequencies (e.g., Ziolkowski and Wright, 2012). Another possibility, because this is a towed system, is noise generated at the receiver electrodes.…”

Section: Application To Marine Csem Datamentioning

confidence: 99%

Wiener estimation of the Green’s function

Ziolkowski

2013

GEOPHYSICS

View full text Add to dashboard Cite

I consider the problem of finding the impulse response, or Green's function, from a measured response including noise, given an estimate of the source time function. This process is usually known as signature deconvolution. Classical signature deconvolution provides no measure of the quality of the result and does not separate signal from noise. Recovery of the earth impulse response is here formulated as the calculation of a Wiener filter in which the estimated source signature is the input and the measured response is the desired output. Convolution of this filter with the estimated source signature is the part of the measured response that is correlated with the estimated signature. Subtraction of the correlated part from the measured response yields the estimated noise, or the uncorrelated part. The fraction of energy not contained in this uncorrelated component is defined as the quality of the filter. If the estimated source signature contains errors, the estimated earth impulse response is incomplete, and the estimated noise contains signal, recognizable as trace-to-trace correlation. The method can be applied to many types of geophysical data, including earthquake seismic data, exploration seismic data, and controlled source electromagnetic data; it is illustrated here with examples of marine seismic and marine transient electromagnetic data.

show abstract

Section: Application To Marine Csem Datamentioning

confidence: 99%

Wiener estimation of the Green’s function

Ziolkowski

2013

GEOPHYSICS

View full text Add to dashboard Cite

show abstract

“…Determining the geophysical properties of the ocean bottom is an old and non trivial problem with scientific as well as engineering implications. Even if electromagnetic approaches exist [4] the large majority of the survey methods are based on the analysis of bottom acoustic returns complemented with in situ observations and historical information. The problem is complicated by the fact that sound velocity strongly varies in consolidated sediments, which makes it difficult to determine at the same time bottom layering (i.e., layer thickness), based on the time-of-flight of acoustic arrivals, and compressional velocities on each layer where those arrivals propagate.…”

Section: Distributed Sensor Arraymentioning

confidence: 99%

The Widely scalable Mobile Underwater Sonar Technology (WiMUST) H2020 project: First year status

Antonelli

Caffaz

Casalino³

et al. 2016

OCEANS 2016 - Shanghai

View full text Add to dashboard Cite

Abstract-The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project is an H2020 Research and Innovation Action funded by the European Commission. The project aims at developing a system of cooperative Autonomous Underwater Vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper reports about the first year activities and it gives an overview of the main objectives and methods.

show abstract

“…For each line, the OBC is static with the source moving to different shot positions. The MTEM method is described by Wright et al (2002Wright et al ( , 2005, Ziolkowski et al (2007), and Ziolkowski and Wright (2012). In the MTEM method, a time-varying current, for example a step function or a pseudorandom binary sequence (PRBS), is injected between two source electrodes and measured; the source signal used at Harding was a PRBS.…”

Section: Multitransient Electromagnetic Datamentioning

confidence: 99%

Predicting controlled-source electromagnetic responses from seismic velocities

2014

View full text Add to dashboard Cite

We created a workflow to predict controlled-source electromagnetic (CSEM) responses from seismic velocities and compared the predicted responses with CSEM data. The first step was to calculate a resistivity model from seismic velocities in a Bayesian framework to account for the uncertainties. The second step was to estimate the electric anisotropy and improve the resistivity model for the depths at which there was no well control. The last step was to use this updated resistivity model to forward-model CSEM responses and compare the result with CSEM data. The comparison with real data revealed that the measured CSEM responses were generally within plus and minus one standard deviation of the predicted responses. This workflow was able to predict CSEM responses, which can prove very useful for feasibility studies before acquisition and interpretation after acquisition of CSEM data.

show abstract

The Potential of the Controlled Source Electromagnetic Method: A Powerful Tool for Hydrocarbon Exploration, Appraisal, and Reservoir Characterization

Cited by 9 publications

References 46 publications

Wiener estimation of the Green’s function

Wiener estimation of the Green’s function

The Widely scalable Mobile Underwater Sonar Technology (WiMUST) H2020 project: First year status

Predicting controlled-source electromagnetic responses from seismic velocities

Contact Info

Product

Resources

About