Johannes Amtmann

Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions

Seismic interpretation can be supported by seismic attribute analysis. Common seismic attributes use mathematical relationships based on the geometry and the physical properties of the subsurface to reveal features of interest. But they are mostly not capable of describing the spatial arrangement of depositional facies or reservoir properties. Textural attributes such as the grey level co-occurrence matrix (GLCM) and its derived attributes are able to describe the spatial dependencies of seismic facies. The GLCM - primary used for 2D data - is a measure of how often different combinations of pixel brightness values occur in an image. We present in this paper a workflow for full three-dimensional calculation of GLCM-based seismic attributes that also consider the structural dip of the seismic data. In our GLCM workflow we consider all 13 possible space directions to determine GLCM-based attributes. The developed workflow is applied onto various seismic datasets and the results of GLCM calculation are compared to common seismic attributes such as coherence.

Mapping directional variations in seismic character using gray-level co-occurrence matrix-based attributes

AbstractTexture attributes describe the spatial arrangement of neighboring amplitudes values within a given analysis window. We chose a statistical texture classification method, the gray-level co-occurrence matrix (GLCM), and its derived attributes, to produce a semiautomated description of the spatial arrangement of seismic facies. The GLCM is a measure of how often different combinations of neighboring pixel values occur. We tested the application of directional GLCM-based attributes for the detection of seismic variability within paleoriver features. Calculation of 3D GLCM-based attributes can be done in 13 space directions. The results of GLCM-based attribute calculation differed depending on the chosen GLCM parameters (number of gray levels, analysis window, and direction of calculation). We specifically focused on how the direction of calculation influenced the computation of attributes, while keeping other parameters constant. We first tested the workflow on a 2D training image and later ran on a ...

Testing of Clustering Algorithms on Different 3D Seismic Models

Summary In seismic interpretation, a big amount of data has to be handled to segment the data cube in zones and faults. In the conventional method, inlines, crosslines and seismic sections are interpreted to divide the geological zones on seismic reflectors and on seismic discontinuities. This segmentation is often guided by seismic attributes, wells and further geological information. The other approach of seismic interpretation is dividing seismic data by algorithms. One popular method to achieve an automatic segmentation is clustering of seismic attributes. There are several clustering algorithms available in all different kinds of scientific disciplines. Some are also already used in seismic interpretation. To get an overview of clustering algorithms and to understand the different kinds of algorithms a research study was done. Therefore, multiple algorithms were classified in a matrix and a workflow was created to test various algorithms on different synthetic 3D seismic data models and subsequently a test environment was founded to understand algorithms to use them for automatic or semiautomatic interpretation of seismic data.