Bluware’s Chief Geologist, Erik Holtar, is featured in EAGE First Break to demonstrate why utilizing deep learning for interpreting the seismic expression of injectites has potential, by delineating the complex features after training input on well-defined examples in the seismic cube.
The geometry and reservoir properties of injectites have always challenged seismic interpreters. The breakthroughs in understanding deposition, formation, and impact from fluid migration, coupled with 3D seismic data in the early 2000’s, have given more ways for geoscientists to assess lithology and fluid nature.
Given decent imaging of the injectite geometry, the actual interpretation is difficult and time consuming using traditional interpretation tools.
The use of artificial intelligence utilizing deep learning for interpreting the seismic expression of injectites can have a large potential. Furthermore, AI-based interpretation enables the use of multiple versions of seismic data, such as angle stacks and different surveys covering the same area. Several alternative versions of the injectites gives the interpreter a better basis for analysis of uncertainty.