Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce terabytes of data. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM.
3-D analysis Amplitude AOGR attenuation Austin Chalk Average Energy AVO below tuning big data DHIs Direct Hydrocarbon Indicators Eagle Ford Envelope faults flat spots GeoExpro geologic patterns geometric attributes Geophysical Insights Gulf of Mexico Iot machine learning multi-attribute analysis multiattribute neural networks pattern recognition PCA principal component analysis seismic attributes seismic computing seismic interpretation Self-Organizing Map Self Organizing Maps SOM SOM analysis spectral decomp spectral decomposition sub-seismic Sweetness thin beds tight carbonate reservoirs unconventional unsupervised neural networks well calibration workflow