Application of texture attribute analysis to 3D seismic data

Abstract

In this study, texture attribute analysis application to 3D surface seismic data is presented. This is done by choosing a cubic texel from the seismic data to generate a grey-level occurrence matrix, which in turn is used to compute secondo rder statistical measures of textural characteristics. The cubic texel is then successively made to glide through the 3D seismic volume to transform it to a plurality of texture attributes. Application of texture attributes to two case studies from Alberta confirm that these attributes enhance understanding of the reservoir by providing a clearer picture of the distribution, volume and connectivity of the hydrocarbon bearing facies in the reservoir.

Ground penetrating radar use in flood prevention

“…It enhances interface between two GPR facies artificially generated degradation zones within embankment structure. Theory and applications of similarity for seismic data are described by Chopra (2007) and Chopra and Alexeev (2005).…”

“…The result of the similarity attribute (Fig. 6) shows continuous coherent reflections depicted by the white patches, which could be correlated with highly saturated soil or the appearance of stratification Attributes in the group of texture that return statistical properties of a grey-level co-occurrence matrix (GLCM) (Chopra and Alexeev 2005;Hall-Beyer 2012) characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. GLCM texture considers the relation between two pixels at a time, called the reference and the neighbor pixel.…”

Application of Advanced Volume Interpretation (AVI) Workflows to Improve Data Quality for Rapid Interpretation

“…Figure 2a shows the satellite image of the seismic survey area with 3D seismic coverage in red, sand dunes in green and sabkha in blue, which are clearly visible in the satellite image. The texture attribute has enabled the interpreter to differentiate the real high seismic amplitudes from the imprint of the sand dunes and sabkha (Chopra and Alexeev, 2005). 2b, clearly revealing the topography of the areas; sand dunes are shown in brown while sabkha are shown in light yellow.…”

A Machine-Aided Seismic Signal Analysis Workflow for Subsurface Faults and Facies Visualization and Interpretation, South Central Anadarko Basin, Oklahoma

“…Texture is an underutilized attribute compared to many attributes utilized in industry today and only recently have seismic texture techniques been used to enhance 3D volumes. Since the 2000s, seismic texture has proven useful in enhancing interpretation capabilities for facies discrimination when compared to amplitude data (Chopra, 2005;Gao, 2004Gao, , 2006. Texture is very useful in extracting quantitative information through statistical measures.…”

“…Historically speaking, texture is an underutilized attribute compared to many attributes used in industry today. Since the 2000's seismic texture has proven useful in enhancing interpretation capabilities for facies discrimination when compared to amplitude data (Chopra, 2005;Gao, 2004Gao, , 2006. In 2011, Gao investigated GLCM vs WMR methods for texture and showed their benefits to well calibration efforts.…”