A new technology that analyses dispersion events in seismic data is presented. The technology aims at identifying both reservoirs and also the likelihood of any presence of liquid hydrocarbons within them. This paper details the science on which the technology is based and empirical results from usage of the technology.
Presence of strong wave dispersion in seismic data has analytically and in tests been seen to correlate with high porosity and permeability formations. A lack of dispersion has conversely been seen to correlate with low porosity systems. Furthermore, a high viscosity fluid in a poro-elastic system has been seen to cause higher dispersion effects compared to brine. This permits derisking of reservoirs to identify locations with high chance of liquid hydrocarbon.
Resonance wave systems are abundant in sedimentary rock. The measurement of resonance waves permits the study of otherwise weak frequency shifts in seismic data, which can then be used to search for reservoir rock and liquid hydrocarbon.
Velocity dispersion and resonance wave analysis of seismic data requires carefully selected wavelet based spectral decomposition methods. Results from a commercially available technology presented in this paper have shown a need to prioritize high accuracy spectral decomposition methods that are able to identify minute dispersion events. These methods are often very computationally demanding. Therefore, those methods need to be selected that ensure highest accuracy while optimizing for speed.
A dispersive event occurs when an incoming P-wave propagates through a heterogeneous porous media due to mesoscopic flow. Dispersivity contributions may also stem from localized effects such as Krauklis waves. The level of dispersivity has in models and field tests been identified as a function of the reservoir porosity, permeability and fluid viscosity. Empirical results from the technology presented here, suggest the ability to identify reservoirs and frequently also their fluid content using dispersion analysis of seismic data.
Case study results using the commercial technology are presented over both discovery and dry wells in Norway and Oman. The results show how new insights into poro-elastic lithology can be provided and also the technology's potential to contribute to an improved overall prospect derisking and field delineation with respect to fluid content.
The technology demonstrates the ability to extract additional information from seismic data sets and thereby further the geological and geophysical subsurface interpretation and modelling.