Iván Dimitri Marroquín
As you scroll through a seismic volume for the interpretation of seismic events of interest, you may start to notice variations in amplitude, frequency, and phase indicated by the change in shape of seismic reflections within a specific interval. Then, you wonder if the variability in reflection shape suggests distinct groups. Each of which can be thought to describe an area with particular geologic characteristics or reservoir properties.
A standard technique for mapping changes in seismic trace shape is seismic facies classification. With this technique, you seek to identify groups of seismic facies, in which patterns in a given group resemble each other more than patterns in other groups. The output is a map encoded with seismic facies groups.
So, you decide to conduct a seismic facies classification study with confidence that the result will extract meaningful insights from complex seismic reflection patterns. But soon, you realize that there are potential issues that could prevent the seismic data from being partitioned into meaningful seismic facies groups. Among these issues:
- The relative quality of the seismic data (e.g., signal-to-noise ratio, frequency content, reflection strength, etc.) that may lead to no well-defined groups of seismic facies.
- The large number of different partitioning algorithms - each of which partitions the seismic trace shapes according to their optimization criterion - determines the quality of the resulting seismic facies groups.
- The subjectivity surrounding the selection of the number of seismic facies groups used for partitioning the seismic data.
I have developed a visual-based framework that not only addresses these challenges but also helps you to gain more confidence when interpreting the results of the seismic facies classification. This framework differs from other approaches in the central role that the interpreter plays. The interpreter is directly involved in a visual data exploration procedure to understand the grouping and distribution of the seismic facies; and how these groups relate to geologic features or reservoir properties. The visual-based framework is divided into three main steps. At the end of the process, the interpreter determines the most suitable partitioning algorithm given the chosen optimal number of seismic facies.
For a comprehensive description of the visual-based framework, the interested reader can freely download the article "A knowledge-integration framework for interpreting seismic facies" by clicking here.
