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Stellarsolve has exclusive rights to resell Paradise in Sub Saharan Africa.
Paradise uses robust, unsupervised machine learning and supervised deep learning technologies to accelerate interpretation and generate greater insights from the data.
ATTRIBUTE GENERATION
Using attributes is fundamental to seismic interpretation. The Paradise Attribute Generator places best-in-class post-stack seismic attribute calculations in the hands of seismic interpreters and specialists alike using easy-to-follow ThoughtFlows. The Paradise attribute library includes a comprehensive list of Instantaneous attributes as well as algorithms and workflows developed by the Attribute Assisted Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma, led by Dr. Kurt Marfurt.
COLOR BLENDING
Blending attributes in an RGB display can dramatically enhance the visibility of geologic features within the seismic data. The Color Blending application is a 3D visualization tool that co-renders three separate attribute volumes (e.g., three spectral decomposition volumes) into a single volume for enhanced seismic interpretation. When applied to seismic data, Color Blending, or co-rendering, highlights geologic and geophysical features, thereby improving interpretability.
ATTRIBUTE SELECTION
Seismic attributes differ in their relative contribution to information (energy) in a given volume. In Paradise, Principal Component Analysis (PCA) is used to identify those attributes that are the most prominent and quantify their relative contribution to a volume. Using an Eigen spectrum chart, the relative contribution is presented both graphically and numerically, taking the guesswork out of selecting the right attributes seismic interpretation.
MACHINE LEARNING STRATIGRAPHIC ANALYSIS
Machine Learning (ML) Stratigraphic Analysis uses the Self-Organizing Map (SOM) unsupervised machine learning process to classify stratigraphic facies and their distributions. Applied at single sample scale in Paradise, this process produces a detailed view of stratigraphy due to the stacking effect of classifying individual examples in attribute space. In addition to identifying thin beds, ML Stratigraphic Analysis also reveals potential fluid effects in seismic data. When used with the unique interactive 2D Colormap in Paradise, the distribution of one or more neural classes are calibrated with geology. The Machine Learning Geobody application then incorporates ML Stratigraphic Analysis results to produce geobodies and calculate volumetrics.
MACHINE LEARNING GEOBODIES
Generating geobodies from machine learning is unique to the Paradise AI workbench. Machine Learning Geobodies quantify the volume of seismic interpretation features, including hydrocarbon pore volume. The value of the tool is obtaining estimated volumetrics early in the seismic interpretation workflow. Geobodies are defined from one or more neurons from Stratigraphic Analysis, Fault Detection, and Seismic Facies classification. The output is a three dimensional visualization of geological features, including potential reservoirs. Using Paradise to identify geobodies empowers interpreters to: 1) Investigate geobodies at the sample level of each neuron; 2) Capture details on areas of interest, including volumetrics and statistics; 3) Edit selected geobodies by filling in areas or pruning extraneous samples; and 4) Export specific geobodies to an interpretation system for further analysis.
WELL LOG VISUALIZATION
The Well Log Visualization capability enables a corroboration among machine learning classification results, seismic attributes, and traditional well logs. Use the new Well Log Visualization capability to: 1) Compare machine learning classification results and seismic attributes to well logs; 2) View well logs, formation tops, and extracted log curves together within a cross-section; and 3) Customize the Well Log view by adding tracks and changing the properties of data. Well Log Visualization is a powerful tool for visually relating machine learning results to reservoir properties computed from conventional well logs.
WELL LOG CROSS PLOTS
The new Cross Plot tool graphically displays the relationship between log curves and machine learning results, revealing pay intervals on the Well Cross Section depth track. Cross Plots highlight specific neurons in conjunction with log values most associated with production. Apply the Well Cross Plot tool to obtain these results quickly and easily: 1) Bring seismic data resolution to borehole log and lithofacies scale in feet/meters via attributes, Machine Learning (ML) Stratigraphic Analysis using Self-Organizing Maps (SOMs), and ML Geobodies; 2) Extract seismic data along the borehole as attributes, SOM results, or probability results, and easily modify the associated color bars; and 3) Display digital well logs, TD charts, formation tops, and cross-sections in straightforward displays. Interact with the companion Well Log Viewer to analyze logs versus machine learning results, and identify which neurons correspond to reservoir petrophysical properties. Examine different topologies versus well logs to determine which machine learning topology is optimum. Then, convert the classes corresponding to production into ML geobodies. Cross plot region below shows where resistivity is higher than 10 ohms and spontaneous potential is less than -50, conditions for a pay interval. The neuron in the prescribed region is shown in purple.
