SeisClass

Identify variations in lithology with seismic facies maps

SeisClass multi-attribute classification software takes the guesswork out of seismic attribute analysis, which reduces cycle time, increases efficiency and lowers risk. The technology supporting SeisClass—fully integrated with the GeoFrame reservoir characterization system—uses the reservoir's seismic attributes to generate class and probability maps. Your geological interpretation is more accurate and your job is easier because the application determines the variations in lithology. Once a given fluid or lithofacies is captured by a set of attributes and calibrated by one or more wells, similar potential reservoirs can be identified in neighboring undrilled structures or basins. All of the data in the GeoFrame database--including interpreted surfaces, attributes and geological markers--are a direct input to the classification process. SeisClass can be used with pre- and post-stack data. With the advent of multi-component and time-lapse data, you can use SeisClass to determine fluid type and monitor fluid migration for complete reservoir characterization.

Look Beyond Wave Shape

SeisClass has multiple classification algorithms that use any combination of seismic attribute grids as the input. Integrated with GeoFrame, the software accesses a large number of new, instantaneous volume attributes that completely represent and capture the seismic signal--going beyond seismic wave shape. Porosity and permeability maps generated in LPM log property mapping software also can be a direct input to the process.

An Innovative Approach to Data Quality Control

The crossplotting tool in SeisClass helps you understand the natural distribution of data clusters in the attribute space. Using the extended range of regression methods and statistical analysis tables and displays, you can refine and optimize the classification process by selecting the optimum set of seismic attributes to solve a given problem. The crossplotting tool and base map are in direct communication. Anomalies on the base map that you recognize can be selected to have their corresponding attribute values highlighted in the crossplotting tool.

Calibrate your maps to well control supervised classification methods require a set of reference data or training data for each output class. You can assign well properties to a given class, such as fluid type, fluid saturation, range of porosity or lithology. The seismic facies are calibrated by the reservoir parameters observed at the well, which offers you refined facies maps.