Unsupervised seismic facies classification: A review and comparison of techniques and implementation

Coléou, T.; Poupon, Manuel; Azbel, Kostia

doi:10.1190/1.1623635

Cited by 247 publications

(81 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When viewed vertically, each prototype vector takes on the appearance of a waveform shape, giving rise to what is called "waveform shape classification" (e.g. Coléou et al, 2003). Figure 4a shows the result using 19 classes labeled by 19 colors uniformly …”

Section: D Som Plotted Against 1d Colorbarmentioning

confidence: 99%

See 1 more Smart Citation

Seismic interpretation of self-organizing maps using 2D color displays

2010

View full text Add to dashboard Cite

ABSTRACT. Classification without supervision of patterns into groups is formally called clustering. Depending on the application area these patterns are called data lists, observations or vectors. For exploration geophysicists, these patterns are usually associated with seismic attributes, seismic waveforms or seismic facies. The main objective of this paper is to show how one of the most popular clustering algorithms -Kohonen self-organizing maps, can be applied to enhance seismic interpretation analysis associated with one and two-dimensional colormaps.

show abstract

Section: D Som Plotted Against 1d Colorbarmentioning

confidence: 99%

“…The SOM (Kohonen, 2001) clustering is one of the most commonly used tools for non-supervised seismic facies analysis, with SOM providing ordered clusters that can be mapped to a gradational colorbar (Coléou et al, 2003).…”

Section: Kohonen Self-organizing Maps (Som)mentioning

confidence: 99%

Seismic interpretation of self-organizing maps using 2D color displays

2010

View full text Add to dashboard Cite

show abstract

“…Various workers document the geophysical application of the SOM. As an early adopter Coléou et al (2003) used it as a tool in seismic interpretation and called it " an essential tool for unsupervised seismic analysis." Similarly, other scholars benefit from SOM method in the seismic-facies analysis (Saraswat and Sen, 2012) and recognition of seismic patterns (Kourki and Riahi, 2014;Yang et al, 1991).…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Arabameri¹,

soleymani²,

Tokhmechi³

2018

Preprint

View full text Add to dashboard Cite

Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. The primary goal of this paper is to improve the accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. Our proposed workflow aims to improve the accuracy of the estimations by clustering the input data based on specific petrophysical characteristics. We show since each major zones at the offset test wells have a distinct compaction trend, empirical constants in Bowers and Tau methods can be calibrated for each cluster rather than the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. To find the best clustering algorithm, we applied and compared five techniques namely, K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical. We found that the self-organizing map (SOM) method provides the best results by maximizing lithology likelihood within each cluster and improve the overall accuracy of the Bowers and Tau methods. This research also aims to provide a systematic comparison of the mentioned clustering algorithms based on their ability in distinguishing various lithofacies. We also try to minimize the user interference in the process of clustering multiple lithofacies and improve the reproducibility of the results and demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

show abstract

“…The seismic facies analysis deals with both the individuation and the geologic interpretation of the geometry, continuity, amplitude, frequency and velocity of the seismic relectors, more than the outer shape of the sedimentary bodies and the seismic facies associations in a depositional sequence [2,[56][57][58][59][60][61]. In the modern development of this methodology, one aim is represented by the recognition of clusters or groups, representative of signiicant variations in the properties of the rocks, in the lithology and in the content of luids.…”

Section: Seismic Stratigraphymentioning

confidence: 99%

Introductory Chapter: An Introduction to the Seismic and Sequence Stratigraphy and to the Integrated Stratigraphy: Concepts and Meanings

Aiello¹

2017

Seismic and Sequence Stratigraphy and Integrated Stratigraphy - New Insights and Contributions

View full text Add to dashboard Cite

Unsupervised seismic facies classification: A review and comparison of techniques and implementation

Cited by 247 publications

References 0 publications

Seismic interpretation of self-organizing maps using 2D color displays

Seismic interpretation of self-organizing maps using 2D color displays

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Introductory Chapter: An Introduction to the Seismic and Sequence Stratigraphy and to the Integrated Stratigraphy: Concepts and Meanings

Contact Info

Product

Resources

About