07FORCE Avseth Seismic facies classification away from well control The role of augmented training d
Channel: Force Seminars
Date: 2018-10-02

Future exploration on the Norwegian shelf will focus on new play models, including stratigraphic traps which are often located down-flank or up-dip from producing structures. Quantitative seismic interpretation will be essential in the hunt for new prospects in these type of settings. A key challenge will be to do facies and fluid classification/prediction away from existing well control. Rock physics combined with basin modelling and sequence stratigraphic principles can be used to create augmented training data and thereby improve machine learning methods that can be more successful in predicting good reservoirs and mature source rocks in underexplored areas. A good understanding of the thermal and pressure history can constrain the range of possible porosities at a given depth and to quantify the degree of sandstone consolidation and source rock maturation. Combining burial history with rock physics models, we can simulate training data for different facies that honour the geological burial at any given location and depth. Sequence stratigraphic analysis can aid in determining prior probablilities of various lithofacies. We show examples of how the use of geologically constrained augmented training data can improve the statistical classification success-rate of AVO data, compared to relying on empirical well log data only.

Dr. Prof. Per Avseth 
Co-Founder & Lead Geophysicist Dig Science AS