Automated salt interpretation: Part 2 — Smooth surface wrapping of volume attribute

Haukås, J.; Bounaïm, A.; Gramstad, Oddgeir

doi:10.1190/segam2017-17739151.1

Cited by 4 publications

(4 citation statements)

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“…The most straightforward method could be to use the salt indicator function proposed by Wu [2016] and to define the three different categories by thresholding it. As an example, Haukås et al [2017] use this type of approach. If enough data points are available (either lithological or boundary observations), it is also possible to consider building the uncertainty envelope directly from these data, using for example the method proposed by Martin and Boisvert [2017].…”

Section: Generation Of the Uncertainty Envelope And Influence On The mentioning

confidence: 99%

“…Most authors also propose to integrate manually picked information to guide the interpretation, either directly [e.g., Wu, 2016] or during the training phase for methods relying on classification algorithms [e.g., Berthelot et al, 2013, Di et al, 2018, Waldeland et al, 2018, Shi et al, 2019. Finally, Haukås et al [2017] propose an interesting approach to avoid overinterpreting seismic images: they extract surface patches only in well-defined boundary zones, and then smoothly connect the patches in the poorly resolved image parts. Such approach remains, however, deterministic as one single interpretation is produced in the end.…”

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confidence: 99%

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Generating variable shapes of salt geobodies from seismic images and prior geological knowledge

2019

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We have developed an implicit method to automatically generate several possible models of salt top surfaces with varying geometries and topologies. This method can be conditioned to available data such as well markers and seismic picks. Because seismic imaging of salt is prone to velocity uncertainty and Fresnel zone effects, the input of the method is a seismic image that is segmented into three regions: salt, sediments, and uncertain. The uncertain region contains the salt boundary, and all further computations focus within this zone. A monotonic scalar field, ranging from zero on the edge of the certain salt body to one on the maximal possible salt boundary, defines the cumulated probability for a point to be outside the salt body. A random scalar field, also bounded between zero and one, is then used as a threshold for the first scalar field. The salt boundary is implicitly defined by the zero isovalue of the difference between the two fields, and it can be further extracted using marching cubes. The random field parameters have a geometric and topological impact on the simulated salt volumes. They can be adapted to reproduce specific geological features, but their inference remains difficult. The application of the method for the reconstruction of diapir boundaries from a set of partly interpreted sections indicates that it is well-suited to honor numerous constraining data while proposing different possible structural scenarios in the uninterpreted parts.

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Section: Generation Of the Uncertainty Envelope And Influence On The mentioning

confidence: 99%

mentioning

confidence: 99%

Generating variable shapes of salt geobodies from seismic images and prior geological knowledge

2019

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“…Wu (2016) proposes to take into account manually picked samples to guide the interpretation in complicated zones. Haukås et al (2017) extract surface patches in the well-defined boundary zones and then connect the different patches by classifying the seismic data into "salt", "sediments", and "uncertain" and extrapolating the surface patches in the "uncertain" region.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a first general workflow that aims at generating different possible models of salt top surfaces from seismic images. As Haukås et al (2017), we partition the seismic volume into "salt", "sediments" and "uncertain" to determine the zone that contains the salt boundary. We then use a perturbed distance-based method to generate different top salt surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Workflow for 3D Stochastic Modeling of Salt from Seismic Images

Clausolles¹,

Collon²,

Caumon³

2018

Proceedings

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Interpreting salt is a complex and time consuming task in seismic interpretation. Considerable progresses have been made recently by automatic methods which detect deterministic salt tops from the seismic image. However, uncertainties about salt exist, owing to intrinsic physical limitations of seismic imaging. Therefore, we propose a new workflow to generate several possible models of salt top surfaces with varying geometries and topologies. The method we propose is divided into three steps. We first segment the seismic volume into three regions: salt, sediments and uncertain, depending on the reliability of the automatic interpretation. We then compute a monotonic scalar field in the uncertain region, ranging from zero at the contact with salt to one at the contact with sediments. We finally generate a random field bounded between zero and one. The salt boundary is implicitly defined by the zero isovalue of the scalar field defined as the difference between the distance and the random fields. Applications of this workflow on a 2D seismic image and a 3D synthetic data set illustrate the potential of the method to efficiently address salt interpretation uncertainties.

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Automated interpretation of top and base salt using deep-convolutional networks

Gramstad

Nickel

2018

SEG Technical Program Expanded Abstracts 2018

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Automated salt interpretation: Part 2 — Smooth surface wrapping of volume attribute

Cited by 4 publications

References 2 publications

Generating variable shapes of salt geobodies from seismic images and prior geological knowledge

Generating variable shapes of salt geobodies from seismic images and prior geological knowledge

A Workflow for 3D Stochastic Modeling of Salt from Seismic Images

Automated interpretation of top and base salt using deep-convolutional networks

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