Fast salt-boundary interpretation with optimal path picking

Wu, Xinming; Fomel, Sergey; Hudec, Michael R.

doi:10.1190/segam2017-17782682.1

Cited by 9 publications

(3 citation statements)

References 25 publications

(28 reference statements)

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“…For the specific problem of saltbody delineation, one approach is to first locate the saltbody by the R‐CNN algorithm (Ren et al . ) and then adjust the bounding box for matching the actual seismic images by the optimal path picking (Wu, Fomel and Hudec ). A more convenient solution is to implement the fully convolutional network (Long, Shelhamer and Darrell ) for real‐time seismic pattern interpretation, including saltbody (Di, Gao and AlRegib ).…”

Section: Discussionmentioning

confidence: 99%

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

AlRegib

2019

Geophysical Prospecting

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Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of three‐dimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter's intelligence. This study first implements two popular machine learning tools, the multi‐layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi‐layer perceptron scheme is capable of efficiently utilizing an interpreter's knowledge by selecting, pre‐conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.

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Section: Discussionmentioning

confidence: 99%

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

AlRegib

2019

Geophysical Prospecting

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“…To avoid interpreter bias, Ramirez et al (2016) adopt the theory of sparse representation (Donoho et al 1998) to minimize intervention from interpreters while automatically segmenting salt structures from 3-D seismic data set. Wu et al (2017) applies the optimal path picking algorithm for salt-boundary delineation from a limited number of key points defined by an interpreter. Meanwhile, considering the insufficiency of a single attribute to reliable salt detection, researchers (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

Shafiq

AlRegib

2018

Geophysical Journal International

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Salt bodies are important subsurface structures with significant implications for hydrocarbon accumulation and sealing in offshore petroleum reservoirs. This study presents an unsupervised workflow for delineating the surface of salt bodies from 3-D seismic surveying based on a multi-attribute k-means cluster analysis. The workflow consists of four steps. First, a suite of seismic attributes are selected and computed from the volume of original seismic amplitude, each of which separates the target salt boundaries from the surrounding non-boundary seismic features in its unique way. Second, two sets of representative samples are manually picked in an interpreter-specified vertical section to help initialize the centres of the boundary and nonboundary clusters. Third, the k-means cluster analysis is performed on the seismic attributes to generate a clustering model for volumetric processing. Finally, applying the built k-means model to every sample in the seismic volume provides us with a probability volume in which the high values indicate the presence of salt-dome boundaries. The effectiveness of the proposed method is verified for interpreting the multiple salt bodies in the F3 seismic data set over the Netherlands North Sea, and the generated salt-boundary volume can serve as input for more advanced salt interpretation, such as salt surface/body extraction, to assist structural framework modelling in the subsalt zones of high geologic complexities and petroleum potentials. We conclude that the proposed workflow paves the way for computer-aided seismic interpretation in a more comprehensive manner by incorporating more advanced machine learning algorithms (e.g. artificial neural network) and targeting other important seismic structures (e.g. fault).

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“…[2013, 2017] use methods inspired from active-contour models [Kass et al, 1988] and level sets [Osher and Sethian, 1988], Wu [2016] solves a screened Poisson equation to compute an implicit salt indicator function and Wu et al [2018] extract an optimal path by solving the eikonal equation. Finally, several authors have also proposed to use various machine learning algorithms [e.g., Berthelot et al, 2013, Di et al, 2018, Waldeland et al, 2018, Shi et al, 2019.…”

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confidence: 99%

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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Fast salt-boundary interpretation with optimal path picking

Cited by 9 publications

References 25 publications

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

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

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