Seismic Attributes, Their Use in Petrophysical Classification

Taner, M. Turhan; Walls, Joel; Taylor, Gareth

doi:10.3997/2214-4609-pdb.15.f-09

Cited by 13 publications

(13 citation statements)

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“…Geophysical features in seismic reflection data can be considerably enhanced with the application of seismic attribute analysis, which can be used to extract qualitative and quantitative information from data and to emphasize the display of structural features and relationships. A seismic attribute is a descriptive and quantifiable parameter (e.g., time or dip) that can be calculated from a single seismic trace within 3D cubes (Taner et al, 1979;Barnes, 1996;Chen and Sidney, 1997;Taner, 2001;Chopra and Marfurt, 2007;Iacopini, 2011;Forte et al, 2016;Dewett et al, 2021;Iacopini and Butler, 2021). Seismic attributes can be computed over pre-stack or post-stack seismic data and, nowadays, over 3D seismic volumes.…”

Section: Background On Data Pre-conditioning Techniques and Seismic A...mentioning

confidence: 99%

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

et al. 2023

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Legacy seismic reflection data constitute infrastructure of tremendous value for basic research. This is especially relevant in seismically hazardous areas, as such datasets can significantly contribute to the seismotectonic characterization of the region. The quality of the data and the resulting image can be effectively improved by using modern tools, such as pre-conditioning techniques and seismic attributes. The latter are extensively used by the hydrocarbon exploration industry, but are still only poorly applied to the study of active faults. Pre-conditioning filters are effective in removing random noise, which hampers the detection of subtle geologic structures (i.e., normal faults). In this study, a workflow including pre-conditioning and extraction of seismic attributes is used to improve the quality of the CROP-04 deep seismic reflection profile. CROP-04 was acquired in the 1980s across the Southern Apennines mountain range, one of the most hazardous seismically active regions in Italy. The results show the capacity of this method to extract, from low-resolution legacy data, subtle seismic fabrics that correspond to a dense network of fault sets. These seismic signatures and the enhanced discontinuities disrupting the reflections, which were invisible in the original data, correlate well with the main regional normal faults outcropping at the surface. Moreover, the data reveal higher structural complexity, due to many secondary synthetic and antithetic structures, knowledge of which is useful in modeling of the local and regional distribution of the deformation and potentially in guiding future field mapping of active faults. This proposed approach and workflow can be extended to seismotectonic studies of other high-hazard regions worldwide, where seismic reflection data are available.

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Section: Background On Data Pre-conditioning Techniques and Seismic A...mentioning

confidence: 99%

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

et al. 2023

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“…These changes are caused by the different physical rock properties of the various rocks in the subsurface. Therefore, they are used to highlight specific geological, physical, and/or reservoir properties, and to help recognize patterns and parameter relationships (Taner, 2001;Chopra & Marfurt, 2007;Veeken, 2007). Seismic single-attributes can be categorized by different taxonomies (Dewett et al, 2021).…”

Section: Seismic Single-attribute Analysismentioning

confidence: 99%

“…Seismic attributes are quantities derived from seismic data, based on e.g., time, amplitude, frequency, phase, velocity, and attenuation (Chopra & Marfurt, 2007). For a long time they have been used for hydrocarbon reservoir characterization, prediction and monitoring, by delivering information on geological structures, lithology, reservoir properties, parameter relationships and patterns that might not be recognized otherwise (Taner, 2001;Chopra & Marfurt, 2007;Sarhan, 2017). For example, Banerjee & Ahmed Salim (2020) used seismic attributes to analyse the structural features and the depositional patterns of the NW Sabah Carbonate Platform in the South China Sea.…”

Section: Introductionmentioning

confidence: 99%

Advanced seismic characterization of a geothermal carbonate reservoir – Insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Wadas

Bauer

Shipilin

et al. 2023

Preprint

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Abstract. The quality of geothermal carbonate reservoirs is controlled by numerous factors and processes, such as the depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution are therefore difficult to predict. For the example of a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate, how an advanced analysis can deliver an improved reservoir model concept and help to identify possible exploitation targets within the Upper Jurassic carbonates. To identify possible reservoir sections and to understand their above-mentioned controlling factors, we analyse different seismic single- and multi-attributes. Some of the seismic attributes, together with lithology logs from wells, are then used to identify parameter correlations between the seismic attributes and the different carbonate lithologies to obtain a supervised neural network based 3D lithology model of the geothermal reservoir. Furthermore, we compare the fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis), to address scalability. Our results show that, for example, acoustic impedance is well suited to identify reefs and karst-related dolines. Areas with strong karstification or fault- and fracture-related deformation, which are both associated with high permeabilities, are also indicated by e.g. strong frequency attenuation, variance anomalies, and/or morphological features like bowl-shaped structures derived from the shape index. Furthermore, by using sweetness we can reconstruct the reef development of two exemplary reefs, and regarding the lithology distribution, we show that the upper part of the reservoir is dominated by limestone and dolomitic limestone rather than dolomite. In addition, we observe spatial trends in the degree of dolomitization. With respect to the fracture orientations on seismic- and well scales, we point out that a general scalability is not possible due to a combination of methodological limitations and geological reasons. Nonetheless, we argue that the combination of both methods provides an improved overall impression of the fracture systems, and therefore possible fluid pathways. By taking all the results into account, we are able to improve and adapt recent reservoir concepts to outline the different phases of its structural and diagenetic evolution. Furthermore, our results help to identify high quality reservoir zones in the Munich area.

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“…Seismic attributes are the main tools used to predict lithology of seismic reflection data, which can act as filters that quantify properties of seismic images (Taner, 2001). There are several studies that apply seismic attributes in order to investigate the presence of gas hydrate in different regions of the world (e.g., Hato et al, 2006;Santos et al, 2009;Oliveira and Oliveira, 2009;Joshi et al, 2017;Aguiar et al, 2019Aguiar et al, , 2021.…”

Section: Introductionmentioning

confidence: 99%

Analysis of seismic attributes to enhance a Bottom Simulating Reflector (BSR) in the gas hydrate area of Umitaka Spur, eastern margin of Japan Sea

et al. 2022

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This work aims to evaluate the best seismic attributes to use to identify the Bottom Simulating Reflector (BSR) of Umitaka Spur, a well-known gas hydrate area in Joetsu Basin, Japan. For this purpose, it uses 2D single-channel seismic data from cruises NT07-20 and NT08-09 provided by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The methodology used for the qualitative analysis of six seismic attributes to highlight the BSR was done using Schlumberger’s Petrel software. These attributes were Envelope, RMS Amplitude, Amplitude Volume Technique (tecVA), Relative Acoustic Impedance, Spectral Decomposition and Instantaneous Frequency. Thus, this study was fundamental to investigate gas hydrates occurrence through the seismic assessment of some geophysical property (amplitude and frequency) and the geological attributes that highlighted the faults of the complex local geology. The application of these attributes can be used in other areas providing an effective tool to enhance the recognition of BSR all over the world.

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Seismic Attributes, Their Use in Petrophysical Classification

Cited by 13 publications

References 0 publications

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

Advanced seismic characterization of a geothermal carbonate reservoir – Insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Analysis of seismic attributes to enhance a Bottom Simulating Reflector (BSR) in the gas hydrate area of Umitaka Spur, eastern margin of Japan Sea

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