Predicting rock properties from seismic data is one of the pillars of dGB. In fact, back in 1995 we started out as a QI (Quantitative Interpretation) specialist company. Our first software system GDI focuses on integrating geology, logs and seismic data and supports stochastic pseudo-well modeling and neural networks for non-linear analysis. GDI is still a work horse for our in-house service projects that cover the entire spectrum of QI work: seismic inversion, fluid-replacement, pre- and post-stack modeling, AVO analysis, 4D etc.
With the development of OpendTect and plugins, we have broadened our technology but our passion for QI remains. More and more work flows originating from GDI are now supported in a more user-friendly way in OpendTect. For example the Neural Networks plugin supports waveform segmentation (visualizing seismic patterns along horizons) as well as inversion along well tracks. In the long run we intend to migrate all GDI functionalities to OpendTect, which should thus become the ultimate QI workbench.
All kind of rock properties can be predicted with the use of the neural networks plugin. It is possible to relate any well log to seismic data. Examples are porosity, Vshale, lithology, permeability.
Deterministic Inversion includes a 3D model builder for the construction of a priori impedance models using well log and seismic horizon data; a 2D error grid generation module to provide spatial inversion constraints and a model-based deterministic acoustic impedance inversion module.
Stochastic Inversion
Stochastic Inversion includes the MPSI UltraFast stochastic inversion module for the generation of multiple geostatistical realizations. This plugin allows the user to explore the impact of the geophysical uncertainties in the inversion process. Utilities for post-processing the multiple realizations are available to create expectation, standard deviation, and (joined) probability and trend volumes. This plugin requires a license for the deterministic inversion plugin to be in place.
ARK CLS Seismic Coloured Inversion enables rapid band-limited acoustic impedance inversion of seismic data. A single convolution inversion operator is derived which optimally inverts the data. The spectrum of the inverted data honours the available well data spectra in a global sense. Generally, traditional inversion methods (e.g. sparse-spike) are time consuming, expensive, require specialists and are not performed routinely by the Interpretation Geophysicist, whereas SCI is rapid, easy to use, inexpensive, robust and does not require expert users. SCI and unconstrained sparse-spike appear to give broadly equivalent results.
Understanding geology is the key to drilling successful wells. This means that one needs to gain understanding in the 3D distribution of lithologies, rock properties and fluid fill in the subsurface. Predicting these distributions from 3D seismic data with control points at well locations is the current industry standard. At dGB we take reservoir prediction a step further by looking beyond the numbers and use geological models to improve our prediction. Also, with the use of neural networks, we avoid using oversimplified linear models which can not accurately describe most rock property relations.