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A Rich Set of Data Analytic and AI Tools for Geoscience

Paradise uses robust, unsupervised machine learning and supervised deep learning technologies to accelerate 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
  • Estimate reserves/resources

 Generate attributes to extract meaningful geological information and as input into machine learning analysis

Highlight geologic features in 3D by blending up to three attribute volumes

Identify attributes having the highest variance and contribution among a set of attributes in a geologic setting

Classify multiple attribute volumes simultaneously utilizing Self-Organizing Maps (SOM), an unsupervised machine learning process

 Display the Neural Classes (topology) and their associated colors resulting from Multi-Attribute Classification that indicate the distribution of related facies

Estimate the volume of reserves/resources and geologic features

Capture facies based on distinctive seismic patterns using Convolutional Neural Network (CNN) deep learning technology

Produce fault probability volumes based on existing fault engines (models) or from interpreter-guided, trained engines

Generate geometric and spectral decomposition attributes on a cluster of compute nodes in a fraction of the time on a single machine

Move data between Paradise and Petrel easily and conveniently

Write and save scripts that can be run by the Paradise AI engine using over 600 geoscience, machine learning, and data analysis commands

Attribute Generation
The Paradise Attribute Generator places best-in-class post-stack seismic attribute calculations in the hands of general 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

•   Instantaneous

•   Dip

•   Structure

•   Amplitude

•   Similarity

•   Curvature

•   Spectral Decomposition

•   Texture

Attribute Selection

Attributes differ in their relative contribution to information (energy) in a given volume. In Paradise, Principal Component Analysis (PCA) is employed 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 easily seen both graphically and numerically, taking the guesswork out of selecting the right attributes for the region.

Multi-Attribute Classification

Multi-Attribute Classification uses the Self-Organizing Map (SOM) machine learning process to improve the 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 may be calibrated with geology. The Machine Learning Geobody application uses the results from Multi-Attribute Classification 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 interactively with the Paradise Universal Viewer to select and isolate specific neurons which have classified a set of seismic attributes from the SOM process This enables the interpretation of neuron-identified geologic features and their geometries.

Color Blending

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 interpretation. When applied to seismic data, Color Blending or co-rendering can highlight geologic and geophysical features, thereby improving interpretability.

Color Blending

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 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

Machine Learning Geobody generation and editing are unique to the Paradise AI workbench. Geobodies are created from one or more neuron groups selected from a Self-Organizing Map (SOM), and are used to generate a three-dimensional visualization of geological features and interpreted or drilled reservoirs. Once generated, industry-standard volumetric calculations are available in the tool to estimate the amount of in-place and/or recoverable oil or gas 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/clean-up selected geobodies by filling in areas or pruning extraneous samples
  • Export areas of interest to an interpretation system


Deep Learning (DL) Fault Detection

The DL Fault Detection application is equipped with general pre-trained deep learning engines (models), enabling their application to a wide range of seismic data without the need of user-provided fault examples for training. Using Graphics Processing Unit (GPU) technology, the tool dramatically reduces the time to identify faults in a volume.

Fault detection result on a seismic survey from the Great South Basin, offshore New Zealand. a) Seismic amplitude data on which fault detection is performed. b) Fault probability from the CNN-based fault detection. c) A state-of-the-art seismic coherence attribute. Note the greatly improved fault continuity, resolution, and reduced noise from the CNN-based fault detection method.

Deep Learning (DL) Seismic Facies Classification

The new DL Seismic Facies classification tool enables the identification of structural and stratigraphic facies patterns based on deep learning technology. Seismic facies and other patterns present on seismic data, such as potential Direct Hydrocarbons Indicators, multiples, etc., can be identified in a seismic volume given the appropriate training. The 3D extent of these features can provide significant and valuable information to the interpretation process.

GPU Computing (GPU)

The new deep learning Seismic Facies Classification and Fault Detection use GPU computing for greatest efficiency.  Likewise, the generation of many of the more computationally intensive seismic attributes are available on GPU, 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 for maximum performance.

Petrel Connector

The Petrel* Connector allows Paradise users to import and export data from Paradise into and out of Petrel.

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

*Petrel is a registered trademark of Schlumberger

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.

Technical Specifications

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
  • 32GB RAM
  • 4GB NVIDIA 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

Contact us for more information regarding technical specifications on client/server installations.

* 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.