Neural Networks and Seismic Reservoir Characterization
In seismic-guided reservoir characterization, the most difficult task is to understand and formulate all factors that significantly influence how a seismic wave responds to a rock. Assumptions and simplifications often result in unrealistic and erroneous solutions. Without these limitations, however, a neural network can find the relationship between seismic attributes and reservoir properties in a self-learning and nonlinear way, providing training data containing all relevant information. Examples from certain Bureau research projects (for example, SGR, Sacroc, and Fullerton) show that ANNs significantly improved the mapping of reservoir properties (for example, Volume shale, porosity, and P-velocity) from 3-D seismic data, in both clastic and carbonate sequences.
For a successful application of neural-network methods, a clear understanding of the underlying physics of the problem is essential. Seismic data must be sensitive enough to the rock property of interest; and fundamental ambiguities, such as tuning effect and fizz-gas confusion, cannot be eliminated without new information. The correct use of seismic attributes also plays a vital role.