Finding natural resources requires studying the underground, which takes months of qualified specialists’ work and many high-tech technologies. Each available technology has limitations. Integration of them and their analysis help suggest the subsurface content with better confidence. However, the risk of failure is still high. What if we could click on a “magic button” that would analyse available data and reveal the subsurface content in a minute?
In 1980, we could for example only dream about some “button” that could convert subsurface-acquired seismic data to relevant colour attributes to help us understand or guess the rocks with similar properties. Today, we have software that gives us attributes in seconds. However, to this day we still do not have that “button” that lets us predict the type of rocks and fluid contained in their pores. We can only make assumptions about the possible content of the subsurface.
The fundamental limitation of the available rock properties prediction lies in the first physical principles from back in 1900. Today, we can overcome this limitation with big computation resources and AI algorithms by rewriting the fundamental description of the conventional physical process.
PSS-Geo aims to enable companies working within geology-related fields predict the type of subsurface rocks and fluids in minutes and not months as it is now. We expect that the results of our research will provide such solutions to others.
PSS-Geo has recently developed an AI-based algorithm to estimate accurate rock properties from post-stack seismic data. The aim of this research project is to extend the algorithm to pre-stack seismic data to get an even better rock properties estimation.
No algorithms and methods are available in conventional physics to estimate non-linked key properties of Velocity and Density of the rocks from seismic data. These properties are mainly used as a basis for further lithology and fluids prediction in oil and gas exploration. All existing approaches of seismic inversions are based on these fundamental assumptions: linearization of the Zoeppritz equation, estimation of impedances or elastic parameters and extraction through regressions of linked Velocity and Density.
The accurate estimation of the Velocity and Density removes the gap between pure seismic structural interpolation and quantitative interpretation (QI). Using only a conventional physical approach leaves QI as supplementary with less trustable methods due to fundamental assumptions/limitations.
The application of the proposed technique for high-resolution shallower seismic will significantly improve geohazard studies for seabed installations. It significantly reduces the required time and resources and achieve a high-resolution velocity field of full frequency band. FWI is in comparison less efficient and can only generate results of limited frequency band. In the field of seabed mining (SBM), it will directly allow the mapping of rock properties with information on the spreading of geo bodies or on accumulation of specific minerals.
The project aims to develop an AI-based pre-stack seismic inversion algorithm with a radically new solution approach. This new algorithm will benefit the estimation accuracy of rock properties and substantially reduce the man-hand influence and processing costs.
Patented technology and peer-reviewed publications will be used as the basic principles behind this R&D project.