Geometric attributes for seismic stratigraphic interpretation

Hoek, Tomas van; Gesbert, Stéphane; Pickens, J.

doi:10.1190/1.3485766

Cited by 38 publications

(7 citation statements)

References 5 publications

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“…Although sensitive to unconformities, coherence vertically smears the response over the computation window and unconformities usually appear as vertical changes in the waveform. Barnes et al (2000) and Hoek et al (2010) propose an unconformity attribute that measures the degree of seismic reflector convergence (or divergence), and thereby highlights the termination areas of an unconformity. Smythe et al (2004) introduce a spectral image of correlative events attribute to obtain stratigraphic details by highlighting discontinuities in band-limited seismic data.…”

Section: Unconformity Detectionmentioning

confidence: 99%

3D seismic image processing for unconformities

Hale

2015

GEOPHYSICS

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In seismic images, an unconformity can be first identified by reflector terminations (i.e., truncation, toplap, onlap, or downlap), and then it can be traced downdip to its corresponding correlative conformity, or updip to a parallel unconformity; for example, in topsets. Unconformity detection is a significant aspect of seismic stratigraphic interpretation, but most automatic methods work only in 2D and can only detect angular unconformities with reflector terminations. Moreover, unconformities pose challenges for automatic techniques used in seismic interpretation. First, it is difficult to accurately estimate normal vectors or slopes of seismic reflectors at an unconformity with multioriented structures due to reflector terminations. Second, seismic flattening methods cannot correctly flatten reflectors at unconformities that represent hiatuses or geologic age gaps. We have developed a 3D unconformity attribute computed from a seismic amplitude image to detect unconformities by highlighting the angular unconformities and corresponding parallel unconformities or correlative conformities. These detected unconformity surfaces were further used as constraints for a structure-tensor method to more accurately estimate seismic normal vectors at unconformities. Finally, using detected unconformities as constraints and more accurate normal vectors, we could better flatten seismic images with unconformities.

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Section: Unconformity Detectionmentioning

confidence: 99%

3D seismic image processing for unconformities

Hale

2015

GEOPHYSICS

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“…I therefore compute an unconformity likelihood image (Figure 6a) from the unfaulted image using the method proposed by Wu and Hale (2015a) and then extract the unconformity surface (Figure 6b) on the ridge of this likelihood image. Such an unconformity surface might also be extracted from the geometric attributes proposed by Van Hoek et al (2010). This extracted unconformity surface is then used as discontinuity constraints for a structure-tensor method (Van Vliet and Verbeek, 1995;Fehmers and Höcker, 2003) to accurately estimate seismic reflector slopes p 2 ðwÞ and p 3 ðwÞ in the inline and crossline directions, respectively, as discussed by Wu and Hale (2015a).…”

Section: Flattening With Unconformitiesmentioning

confidence: 99%

Building 3D subsurface models conforming to seismic structural and stratigraphic features

2017

GEOPHYSICS

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Subsurface modeling from seismic and borehole data is important for reservoir prediction, geophysical exploration, and production. A reasonable model should honor borehole rock properties and conform to seismic structural and stratigraphic features. Such a subsurface model can be difficult to build in cases complicated by faults and unconformities. Automatic and semiautomatic methods have been proposed to build subsurface models from seismic and borehole data; however, seismic structural and stratigraphic features and borehole measurements are not fully used in most methods. I have developed a workflow to fully use seismic and borehole data to build subsurface models that honor borehole measurements and conform to seismic horizons, faults, unconformities, and stratigraphic features such as channels. In this workflow, I first automatically remove the faulting and folding in seismic and borehole data and map them into an unfaulted and flattened space, in which seismic reflectors and borehole measurements corresponding to the same geologic layers are horizontally aligned. I then build a subsurface model in this unfaulted and flattened space by computing a sequence of 2D horizontal interpolations of borehole data. Each horizontal interpolation is guided by the stratigraphic features apparent in the corresponding horizontal seismic slice, so that the interpolant conforms to the seismic stratigraphic features. I finally map the interpolated model back into the input space and obtain a subsurface model that honors the seismic and borehole data. I demonstrate the proposed workflow using synthetic and real examples complicated by faults and unconformities.

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“…To obtain complete unconformities from a seismic image, we want to extract the angular unconformities with reflector terminations and the corresponding parallel unconformity or correlative conformity with conformable reflectors. Most automatic methods can detect only angular unconformities by computing attributes such as seismic coherence (Bahorich and Farmer, 1995) and seismic reflector convergence or divergence (Barnes et al, 2000;Hoek et al, 2010) to highlight areas of reflector terminations. Ringdal (2012) proposes a 2D flow-based method to compute unconformity probability that can highlight termination areas and parallel unconformity or correlative conformity.…”

Section: Unconformity Interpretationmentioning

confidence: 99%

“…To obtain complete unconformities from a seismic image, we want to extract angular unconformities with reflector terminations and the corresponding parallel unconformity or correlative conformity with conformable reflectors. Most automatic methods (Bahorich and Farmer, 1995;Barnes et al, 2000;Smythe et al, 2004;Hoek et al, 2010) can detect only angular unconformities. Here, we use .…”

Section: Unconformity Processingmentioning

confidence: 99%

Automatically interpreting all faults, unconformities, and horizons from 3D seismic images

Hale

2016

Interpretation

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Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although others automate the extraction of each type of these surfaces to some extent, it is difficult to automatically interpret a seismic image with all three types of surfaces because they could intersect with each other. For example, horizons can be especially difficult to extract from a seismic image complicated by faults and unconformities because a horizon surface can be dislocated at faults and terminated at unconformities. We have proposed a processing procedure to automatically extract all the faults, unconformities, and horizon surfaces from a 3D seismic image. In our processing, we first extracted fault surfaces, estimated fault slips, and undid the faulting in the seismic image. Then, we extracted unconformities from the unfaulted image with continuous reflectors across faults. Finally, we used the unconformities as constraints for image flattening and horizon extraction. Most of the processing was image processing or array processing and was achieved by efficiently solving partial differential equations. We used a 3D real example with faults and unconformities to demonstrate the entire image processing.

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Geometric attributes for seismic stratigraphic interpretation

Cited by 38 publications

References 5 publications

3D seismic image processing for unconformities

3D seismic image processing for unconformities

Building 3D subsurface models conforming to seismic structural and stratigraphic features

Automatically interpreting all faults, unconformities, and horizons from 3D seismic images

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