Over the last few years, because of the increase in low cost computer power, individuals and companies have stepped up investigations into the use of machine learning in many areas of E&P. For the geosciences, the emphasis has been in reservoir characterization, seismic data processing and most recently, interpretation.
By using statistical tools such as Attribute Selection, which uses Principal Component Analysis (PCA), and Multi-Attribute Classification using Self Organizing Maps (SOM), a multi-attribute 3D seismic volume can be “classified.” PCA reduces a large set of seismic attributes to those that are the most meaningful. The output of the PCA serves as the input to the SOM, a form of unsupervised neural network, which when combined with a 2D color map facilitates the identification of clustering within the data volume.
The application of SOM and PCA in Paradise will be highlighted through a case study of the Niobrara unconventional reservoir. 100 square miles from Phase 5 of Geophysical Pursuit, Inc. and Fairfield Geotechnologies’ multiclient library were analyzed for stratigraphic resolution of the Niobrara chalk reservoirs within a 60 millisecond two-way time window. Thirty wells from the COGCC public database were available to corroborate log data to the SOM results. Several SOM topologies were generated and extracted within Paradise at well locations. These were exported and run through a statistical analysis program to visualize the neuron to reservoir correlations via histograms. Chi2 squared independence tests also validated a relationship between SOM neuron numbers and the presence of reservoir for all chalk benches within the Niobrara.