Multidimensional attribute analysis and pattern recognition for seismic interpretation
References (28)
- et al.
Pattern space of seismic anomalies associated with hydrocarbon deposits
Geophys. Prospect.
(1979) Seismic stratigraphic exploration—Part I
Geophysics
(1971)Seismic stratigraphic exploration—Part II
Geophysics
(1971)Seismic stratigraphic exploration—Part III
Geophysics
(1971)- et al.
Geophysics Signal Analysis
(1980) A first course in geophysical exploration and interpretation
(1978)- et al.
Seismic signal processing
Ancient Sedimentary Environments—A Brief Survey
(1970)- et al.
Seismic stratigraphy and global changes of sea level. Part 6: stratigraphic interpretation of seismic reflection patterns in depositional sequences
- et al.
Interpretation of depositional facies from seismic data
Geophysics
(1979) Inferring stratigraphy from seismic data
Bull. Am. Ass. Petrol. Geol.
(1976)
Array processing in exploration seismology
Complex seismic trace analysis
Geophysics
Elastic wave velocities in heterogeneous and porous media
Geophysics
Velocity log interpretation: the effect of rock bulk compressibility
Soc. Petrol. Engrs. J.
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Application of multi-attribute matching technology based on geological models for sedimentary facies: A case study of the 3rd member in the Lower Jurassic Badaowan Formation, Hongshanzui area, Junggar Basin, China
2022, Petroleum ScienceCitation Excerpt :Rummerfield (1954) applied seismic attributes to oil and gas exploration for the first time and accurately predicted the relevant faults in the fracture region. During the 1970s and 1980s, the seismic attributes most used in petroleum exploration were amplitude-based instantaneous attributes (Justice et al., 1985; Balch, 2012). However, in the 1990s, seismic attribute technology dramatically advanced in every aspect of hydrocarbon exploration and development (Chopra and Marfurt, 2005).
Seismic attribute analysis to enhance detection of thin gold-bearing reefs: South Deep gold mine, Witwatersrand basin, South Africa
2013, Journal of Applied GeophysicsCitation Excerpt :These tools have played an integral part in improving the quality and efficiency of 3D seismic interpretations. They are typically extracted along seismic traces to reveal information that is hidden in the migrated seismic sections (Barnes, 1991; Chopra and Marfurt, 2007; Chopra et al., 2006; Justice et al., 1985; Knapp, 1990; Taner, 2001). Instantaneous frequency is a measure of how the instantaneous phase changes, that is, how quickly the seismic wavelet goes from zero crossing to zero-crossing or peak to trough.
Fuzzy classification of roof fall predictors in microseismic monitoring
2010, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :It seems logical to suggest circumventing the difficulty of obtaining accurate measurements of source parameters by implementing pattern recognition methods and artificial intelligence techniques to identify hazardous seismic events. Pattern classification techniques have previously been applied to seismic records [14]. A number of the earlier works focused on texture analysis methods in order to, for example, detect mineral rich areas [15,16] or to detect bright spots in seismograms [17,18].
Data science applications in oil and gas exploration: An in-depth perspective
2019, Proceedings of Institution of Civil Engineers: Energy