A Rich Set of Data Analytic and AI Tools for Geoscience
Paradise uses robust, unsupervised machine learning and supervised deep learning technologies to accelerate seismic interpretation and generate greater insights from seismic and well data.
Apply the guided ThoughtFlows™ in the Paradise AI workbench to…
Identify and calibrate detailed stratigraphy
Classify seismic facies
Reveal fracture trends
Detect faults automatically
Distinguish thin beds below conventional tuning
Interpret Direct Hydrocarbon Indicators
Generate attributes to extract meaningful geological information and as input into machine learning analysis
Identify attributes having the highest variance and contribution among a set of attributes in a geologic setting
Display the Neural Classes (topology) and their associated colors resulting from Multi-Attribute Classification that indicate the distribution of related facies
Capture facies based on distinctive seismic patterns using Convolutional Neural Network (CNN) deep learning technology
Compare machine learning classification results and other seismic attributes to traditional well logs
Generate geometric and spectral decomposition attributes on a cluster of compute nodes in a fraction of the time on a single machine
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™ and geoscience-focused visualization capabilities. The Paradise attribute library includes a comprehensive list of Instantaneous attributes as well as commercial algorithms and workflows developed by the Attribute Assisted Seismic Processing and Interpretation (AASPI) group at the University of Oklahoma, led by Dr. Kurt Marfurt.
Families of Attributes
- Spectral Decomposition
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.
Multi-Attribute Classification uses the Self-Organizing Map (SOM) unsupervised machine learning process to improve the seismic interpretation of stratigraphic facies and their distributions. Applied at single sample scale in Paradise, this technique has demonstrated the ability to resolve thin beds that are below classic seismic tuning thickness. The SOM methodology has also proven to define potential fluid effects on seismic data given the appropriate seismic attributes. When used with the unique interactive 2D Colormap in Paradise, the distribution of one or more neural classes can be calibrated with geology. The Machine Learning Geobody application uses the results from Multi-Attribute Classification and the deep learning tools in Paradise to detect geobodies and calculate volumetrics.
(Left) Amplitude data. (Right) multi-attribute classification on 100ms stratigraphy above the Buda in the Eagle Ford group
Seismic data owned and provided courtesy of Seitel, Inc.
Multi-attribute classification with low probability (<10%) anomalies (white) in the greater Niobrara section. For more information, read Thin Beds and Anomaly Resolution in the Niobrara by Rocky Roden.
Interactive 2D Colormap
Paradise is the only software product in the industry with a 2D Colormap representing neuron classifications of attributes. The 2D Colormap is used with the Paradise Universal Viewer to select and isolate specific neurons that have classified multiple seismic attributes from the SOM process. The 2D Colormap enables the seismic interpretation of neuron-identified geologic features and their geometries while providing the relative contribution of each seismic attribute per neuron.
The blending of 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 can highlight geologic and geophysical features, thereby improving interpretability..
Machine learning geobodies are isolated at the bin level, enabling volumetric estimates of hydrocarbons
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 either a Multi-Attribute or Seismic Facies classification. The output is a three-dimensional visualization of geological features and potential reservoirs. Once generated, industry-standard volumetric calculations are applied to geobodies to estimate in-place and recoverable hydrocarbons where reservoir data is available. Using Paradise to identify geobodies empowers interpreters to:
- Investigate geobodies at the sample level of each neuron
- Capture details on areas of interest, including volumetrics and statistics
- Edit selected geobodies by filling in areas or pruning extraneous samples
- Export specific geobodies to an interpretation system for further analysis
Deep Learning (DL) Fault Detection
Built on 3D CNN technology, the Deep Learning (DL) Fault Detection application is equipped with a robust synthetic fault model supporting a wide range of seismic data without the need for user-provided fault examples for training. AI Fault Detection in Paradise uses a combination of supervised Deep Learning (DL) and unsupervised Machine Learning (ML) technologies to produce a refined fault volume, ready to import into an interpretation system. Fault Detection in Paradise offers these advantages:
- Seismic fault interpretation is now automatic with AI Fault Detection in Paradise
Generates fault probability and fault neurons – shortens fault interpretation time
Eliminates uncertainty in manually selecting faults to build an ML model
Uses semi-supervised deep learning and machine learning fault interpretation
Seismic interpreters use AI Fault Detection in Paradise to quickly compare aggressive and conservative results, providing more time to refine a fault interpretation. Using local Graphics Processing Unit (GPU) technology, AI Fault Detection in Paradise generates fault volumes based on accurate, detailed synthetic models applicable to any geologic setting. Infilling a fault model is eliminated, and bias is removed from the manual process. This break-through technology produces something entirely new to the industry: Fault Neurons, which classify fault systems. Use the unique 2D Colormap in Paradise to isolate and interact with systems of faults and view faults along with stratigraphic results from machine learning. The powerful combination of deep learning and machine learning refines fault volumes fast and accurately, including in complex fault regimes. Fault volumes generated in Paradise are brought into Petrel easily using the Paradise-Petrel Connector or exported as SEGY volumes for use in other seismic interpretation systems.
Paradise AI Fault Detection (left) run on the seismic data from the Canning Basin in Western Australia (right). Note the detail fault system identified through an unsupervised deep learning CNN process – generated rapidly vs. traditional method of hand-picking lines.
Deep Learning (DL) Seismic Facies Classification
The new DL Seismic Facies classification tool enables the identification of structural and stratigraphic facies patterns based on supervised, deep learning (CNN) technology. Seismic facies and other patterns present in seismic data, such as potential Direct Hydrocarbon Indicators, multiples, etc., can be identified in a seismic volume through training of the engine (model) on the desired facies. The 3D extent of these features can provide significant and valuable insights into the seismic interpretation process.
Well Log Visualization
The new Well Log Visualization capability in Paradise 3.4 enables a corroboration among machine learning classification results, seismic attributes, and traditional well logs. Seismic data is presented at borehole and lithofacies resolution in feet/meters. Use the new Well Log Visualization capability to:
- Compare machine learning classification results and other seismic attributes to traditional well logs
- View well logs, formation tops, and extracted log curves together within a cross-section
- 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. Access a technical paper via the button below on correlating machine learning classification results to the presence of a reservoir.
GPU Computing (GPU)
Many of the more computationally intensive processes in Paradise use GPU computing to accelerate seismic interpretation. The new deep learning processes that use convolutional neural network (CNN) technology, and many of the seismic attributes, are examples where GPU computing is applied to deliver performance. And more attributes in the rich Paradise library are being migrated from multi-threaded CPU to GPU processing.
Contact us to learn more about how Paradise leverages GPU computing to obtain maximum performance and enhance the experience of geoscientists.
The Paradise – Petrel* Connector allows seismic interpreters to import and export data to and from Paradise seamlessly and quickly, increasing the efficiency of seismic interpretation using machine learning.
The Petrel Connector to Paradise is installed on the same machine with Petrel and appears as an icon on the Petrel ribbon. Simply open the Paradise Connector, select the data to be moved to Paradise, drag and drop the data items onto the Petrel Connector dialog, and select Export. Import from Paradise to Petrel is equally straightforward. Multiple items can be exported at once by selecting the folder to export, such as all wells. The products of machine learning and deep learning in Paradise, such as Deep Learning Seismic Facies, Faults, and Multi-Attribute Classification volumes can be imported easily into Petrel for further analysis.
*Petrel is a Registered Trademark of Schlumberger Limited
Paradise Scripting Language
The Paradise Scripting Language (PSL) is a powerful procedural and interpreted language for geophysical signal and neural network analysis. The language includes over 600 geoscience-specific commands for analyzing geoscience data. PSL is designed for geoscientists and researchers to develop new geophysical data procedures. Unlike Python or other programming tools, many geoscience constructs are pre-built and readily available in PSL, saving time while providing a rich toolset for analysis and investigations.
The Paradise Script Processor (PSP) is a software application for optimized multi-thread vectorized performance. PSP modes include interactive operations, numerical batch processing, and multi-batch parallel processing. Included with PSL is the Paradise Script Editor (PSE) to write and run scripts interactively.
Below is a list of the minimum technical hardware and software requirements for running a single instance of the Paradise client and server on a dedicated workstation.
Windows 10 Pro
MS SQL Server 2012
8+ processing cores
8GB NVIDIA Quadro graphics card
File storage capacity depends on several different variables: See note below*
10Gb ethernet connection for networked data storage or USB 3.0 connection for external storage devices
*File Storage capacity is dependent on the size of the surveys and attributes volumes, plus interpretation files, e.g., faults, horizons, wells, etc. Local storage (on the server running Paradise) is encouraged for evaluation purposes; however, if a centralized network storage facility is to be used for project files, a 10 Gb LAN connection is recommended between the target server and project file storage facility.