Facies Modeling (LITHANN®) -Seismic - Technology
Statistical Multi-Attribute Neural Network analysis
In some cases, a deterministic impedance based inversion process may not be suitable. Reasons for this could include:
- Poor quality well and/or seismic data. e.g. high random noise content; weak reflectors..
- Lack of resolution e.g. deep targets with insufficient vertical or lateral resolution and aperture.
- No separation of rock and fluid properties in the elastic domains. (Modeling can help predict this prior to beginning an expensive AVO or Impedance inversion.)
In such cases, we offer a multi-attribute neural network workflow, designed to provide seismic facies descriptions calibrated to well-data. Workflow elements include:
Attribute Computation
A variety of seismic attribute volumes can be computed. We generally chose Hilbert attributes of envelope, frequency and phase. Relative acoustic impedance and seismic waveform attributes may be added depending on the geological setting and project objective.
Facies Modeling (LITHANN®)
The individual attribute volumes are combined into a single output using an unsupervised neural network classifier. A 2D rectangular Kohonen topology is employed. Calibration of classes is performed at well-locations where known facies are sampled. The output classes are combined and re-numbered to yield a calibrated seismic facies volume
The resulting 3D seismic facies volume is examined within a powerful 3D visualization system to yield geological interpretations of the subsurface structure and properties.