Seismic texture

Summary. This chapter introduces the concept of seismic texture analysis. Several seismic textures and their geological messages are described with respect to depo-sitional history and reservoir quaUty. Finally, a strategy of how to automatically map these textures is recommended and limitations of this method are mentioned. 

The mapping of seismic textures is a very important step in interpreting seismic data because the seismic texture contains information about the reservoir quality. Until recently seismic texture mapping has been done manually which is a time consuming procedure. Automated seismic texture analysis has been proven to be a powerful tool to quickly and precisely map distinct seismic textures. However, some limitations exist that one should be aware of. 

Recommendations. As discussed above, automatic seismic texture analysis is a very powerful tool in extensional tectonic domains and in tectonically undisturbed areas. There it quickly sorts the data set into a number of seismic facies. Compressional tectonic environments, however, are prone to alter the seismic texture and can lead, when applied uncritically, to wrong conclusions with respect to the reservoir quality. When automatic seismic texture analysis is applied to compressional regimes, the results should be interpreted carefully. To avoid wrong conclusions, the seismic data set under consideration should be interpreted by a geologist before automatic texture algorithms are applied to map out potential areas of error. 

Introduction to Seismic Texture 4.6 Mounded Textures in Other Settings... 

A few moves towards texture attribute extraction in seismic data are presented by Sheriff et al. [15]. The probably most common seismic texture... 

J. Schlaf, T. Randen and L. Spnneland (2004) Introduction to seismic texture. This volume. 

Seismic Texture Attributes. Subsection 1.2 introduced seismic sequence stratigraphy as a means of explaining the structure of the subsurface, helping determine the depositional environments and possible rock type distributions. Essentially, this process boils down to an analysis of seismic bodies defined by their internal textures and external shape, often referred to as seismic facies analysis. This type of analysis is a must in seismic interpretation to locate potential reservoirs, especially in complex oilfields. 

For further information, extensive overviews of the interplay between seismic facies analysis and seismic texture attributes are given in the chapter [8] of Carrillat and Vallfe. 

A. Carrillat, T. Randen, L. Spnneland, and G. Elvebakk (2002) Automated mapping of carbonate mounds using 3D seismic texture attributes. Proceedings of the Society of Exploration Geophysicists, Annual Meeting. 

In our approach, the 3D nature of the seismic data is preserved. The analysis is made using a neural network algorithm producing a 3D classification output. The value of this approach to 3D seismic texture mapping has already been demonstrated in the analysis of gas chimneys [25, 34]. Distinct advantages of the approach are that (1) it is independent from and requires no previous horizon interpretation, (2) multiple attributes are simultaneously... 

D seismic texture attributes presented in this chapter are subdivided into two groups. The first includes kinematic texture attributes that capture the reflector orientation or the reflector continuity information. The second defines dynamic texture attributes that capture features in the seismic signal... 

The traditional approach of seismic attribute computation is to extract attributes along vertical traces, irrespective of any dipping nature of the reflections. This industry standard clearly implies a risk of introducing artefacts, when the stratigraphic pattern is not layer cake and flat. As a more consistent alternative, seismic texture attributes compensate for the dip and azimuth or make the attribute extraction invariant to local dip and azimuth. In addition, they are genuine 3D with no trace bias as opposed to coherency and semblance attributes. Moreover, these attributes are amplitude-invariant. 

A geometrical tensor is used in 3D seismic texture attributes for dip and azimuth estimation. This local dip and azimuth estimation (local orientation estimation) approach is based on three steps (see [35, Section 2]) ... 

The general inversion scheme for reservoir characterisation and delineation from seismic multicomponent data involves the transformation of the converted shear waves (PS data) to PP time domain. This operation is performed in order to have the multi-component data with the same time reference for allowing direct comparison and analysis of both PP and PS data. The transformation of PS data to PP time requires a detailed analysis of the overburden, and interpretation of correlative reflection events on both data sets. The next step in the general inversion scheme involves 3D seismic facies analysis using seismic texture attributes as described earlier. 

A three-dimensional seismic facies model could be built from supervised classification of seismic textures that captures the detailed structural framework of the reservoir and its complex architecture. 

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