Abstract: A method for stratigraphic analysis of seismic data comprises: selecting a seismic data volume comprising a plurality of samples; selecting a number of horizons within the seismic data volume; selecting a scale of interest within the seismic data volume, which ranges from 500 m up to the size of the entire seismic data volume; applying an attribute function to at least one of the selected number of horizons at the selected scale of interest; and displaying a stratigraphic attribute, which represents a geologic feature, such as a seismic facies attribute, an unconformity attribute and/or a thinning attribute that highlights a rate of thickness change of a formation layer between adjacent horizons, as a result of applying the attribute function at the selected scale of interest. These attributes facilitate understanding of moderate to large-scale seismic geometries (500 m to 50 Km) and basin development over geologic time.

Abstract: A method for stratigraphic analysis of seismic data, known as GeoLeaf, comprises: selecting a seismic data volume comprising a plurality of samples; selecting a number of horizons (TH, BH1 TPH, BPH, SMH) within the seismic data volume; selecting a scale of interest within the seismic data volume, which ranges from 500 m up to the size of the entire seismic data volume; applying an attribute function to at least one of the selected number of horizons (TH, BH, TPH, BPH, SMH) at the selected scale of interest; and displaying a stratigraphic attribute, which represents a geologic feature, such as a seismic fades attribute (FM), an unconformity attribute (Zmax/Z) and/or a thinning attribute (Z1/Z2).

Abstract: A method for interpreting a plurality of m-dimensional attribute vectors (m2) assigned to a plurality of locations in an n-dimensional interpretation space (n1), which method comprises arranging at least a subset of the attribute vectors as points in an m-dimensional attribute space; defining k classes (k2) of attribute vectors by identifying for each class at least one classification point in attribute space; postulating a classification rule for points in attribute space; determining a class-membership attribute of a point in attribute space using the classification points and the classification rule to obtain a classified point; and assigning a display parameter to the classified point which is related to the class-membership attribute. In one embodiment the display parameter is a mixed display parameter derived from probabilistic membership values each representing a probability that the classified point belongs to a selected class.

Abstract: A method for interpreting a plurality of m-dimensional attribute vectors (m2) assigned to a plurality of locations in an n-dimensional interpretation space (n1), which method comprises arranging at least a subset of the attribute vectors as points in an m-dimensional attribute space; defining k classes (k2) of attribute vectors by identifying for each class at least one classification point in attribute space; postulating a classification rule for points in attribute space; determining a class-membership attribute of a point in attribute space using the classification points and the classification rule to obtain a classified point; and assigning a display parameter to the classified point which is related to the class-membership attribute. In one embodiment the display parameter is a mixed display parameter derived from probabilistic membership values each representing a probability that the classified point belongs to a selected class.