A new methodology based on seismic facies analysis and litho‐seismic modeling: The Elkhorn Slough field pilot project, Solano County, California

Poupon, Manuel; Azbel, Kostia; Palmer, George Herbert

doi:10.1190/1.1821260

Cited by 15 publications

(6 citation statements)

References 1 publication

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“…SOM becomes a widely used unsupervised network, and researchers have successfully applied SOM to multiple attribute analysis. Poupon et al (1999) perform one of the earliest SOM-based seismic facies analyses, and they find that the shape variation of seismic traces is a good indicator of the sand thick- ness as well as the gas saturation. Strecker and Uden (2002) cluster poststack seismic attributes into four different Kohonen SOM runs.…”

Section: Thickness Estimation By Clustering the Multiple-frequency Comentioning

confidence: 99%

The thickness imaging of channels using multiple-frequency components analysis

Yan

Zhang

Niu³

et al. 2019

Interpretation

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Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green (RGB) color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components (formations with the largest thickness value) with a red color, medium-frequency components (formations with a medium thickness value) with a green color, and high-frequency components (formations with the smallest thickness value) with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same result of RGB blending may come from different sets of three frequency components. As a result, the same blended color may correspond to several different time thicknesses. It is also very difficult to interpret the corresponding thickness of the blended colors such as white and yellow. To avoid the ambiguity of time-thickness estimation using RGB blending, we have estimated the time thickness of the thin beds using all of the frequency components in a user-defined frequency band instead of only three frequency components. Our workflow begins with the normal seismic spectral decomposition. Considering that the different reflectivity pairs with a different time thickness have a different amplitude spectrum, we then use the self-organizing map to cluster the decomposed amplitude spectra of seismic traces. We finally assign each cluster with a relative thickness by comparing the clustered results with well logs.

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Section: Thickness Estimation By Clustering the Multiple-frequency Comentioning

confidence: 99%

The thickness imaging of channels using multiple-frequency components analysis

Yan

Zhang

Niu³

et al. 2019

Interpretation

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show abstract

“…Seismic attributes serve as input to modern seismic classification techniques. Most geophysicists are familiar with SOM as a waveform classification technique, in which the input "attributes" are amplitudes along adjacent phantom horizon slices (Poupon et al, 1999). Coléou et al (2003) and Strecker and Uden (2002) show how a suite of attributes can form a vector at each voxel.…”

Section: Som and Dpsommentioning

confidence: 99%

Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps

Zhao

Zhang

et al. 2016

Interpretation

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Recent developments in seismic attributes and seismic facies classification techniques have greatly enhanced the capability of interpreters to delineate and characterize features that are not prominent in conventional 3D seismic amplitude volumes. The use of appropriate seismic attributes that quantify the characteristics of different geologic facies can accelerate and partially automate the interpretation process. Self-organizing maps (SOMs) are a popular seismic facies classification tool that extract similar patterns embedded with multiple seismic attribute volumes. By preserving the distance in the input data space into the SOM latent space, the internal relation among data vectors on an SOM facies map is better presented, resulting in a more reliable classification. We have determined the effectiveness of the modified algorithm by applying it to a turbidite system in Canterbury Basin, offshore New Zealand. By incorporating seismic attributes and distance-preserving SOM classification, we were able to observe architectural elements that are overlooked when using a conventional seismic amplitude volume for interpretation.

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“…Interpretation / November 2013 SB117 workflow was introduced by Poupon et al (1999) in correlating wells to seismic waveforms, where the supervision was not only the actual seismic about the well, but also a suite of synthetic seismic traces generated through petrophysical modeling and fluid substitution.…”

Section: Supervised Classificationmentioning

confidence: 99%

Characterizing a Mississippian tripolitic chert reservoir using 3D unsupervised and supervised multiattribute seismic facies analysis: An example from Osage County, Oklahoma

2013

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Seismic interpretation is based on the identification of reflector configuration and continuity, with coherent reflectors having a distinct amplitude, frequency, and phase. Skilled interpreters may classify reflector configurations as parallel, converging, truncated, or hummocky, and use their expertise to identify stratigraphic packages and unconformities. In principal, a given pattern can be explicitly defined as a combination of waveform and reflector configuration properties, although such “clustering” is often done subconsciously. Computer-assisted classification of seismic attribute volumes builds on the same concepts. Seismic attributes not only quantify characteristics of the seismic reflection events, but also measure aspects of reflector configurations. The Mississippi Lime resource play of northern Oklahoma and southern Kansas provides a particularly challenging problem. Instead of defining the facies stratigraphically, we need to define them either diagenetically (tight limestone, stratified limestone and nonporous chert, and highly porous tripolitic chert) or structurally (fractured versus unfractured chert and limestone). Using a 3D seismic survey acquired in Osage County Oklahoma, we use Kohonen self-organizing maps to classify different diagenetically altered facies of the Mississippi Lime play. The 256 prototype vectors (potential clusters) reduce to only three or four distinct “natural” clusters. We use ground truth of seismic facies seen on horizontal image logs to fix three average attribute data vectors near the well locations, resulting in three “known” facies, and do a minimum Euclidean distance supervised classification. The predicted clusters correlate well to the poststack impedance inversion result.

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A new methodology based on seismic facies analysis and litho‐seismic modeling: The Elkhorn Slough field pilot project, Solano County, California

Cited by 15 publications

References 1 publication

The thickness imaging of channels using multiple-frequency components analysis

The thickness imaging of channels using multiple-frequency components analysis

Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps

Characterizing a Mississippian tripolitic chert reservoir using 3D unsupervised and supervised multiattribute seismic facies analysis: An example from Osage County, Oklahoma

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