New Paradise Logo w Text White 5 June 2018

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
  • Estimate reserves/resources
Attribute Generation

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

RGB Blending

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

2D Colormap

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

Seismic Facies

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

Compare machine learning classification results and other seismic attributes to traditional well logs

GPU Computing

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

Petrel Connector

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

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.

Spectral Decomp-01

Families of Attributes

  • Instantaneous
  • Dip
  • Structure
  • Amplitude
  • Similarity
  • Curvature
  • Spectral Decomposition
  • Texture
Eigen Spectrum Chart

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.

Multi-Attribute Classification

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.

Multi-Attribute Classification

(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.

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

RGB Cube
RGB Map Transparent@2x
Geobodies Paradise Software Optimized

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 CNN technology, the Deep Learning (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 for user-provided fault examples for training. Using Graphics Processing Unit (GPU) technology, the tool dramatically reduces the time to identify faults in a volume, accelerating the seismic interpretation workflow. Seismic interpreters use the Fault Detection application to compare aggressive and conservative results quickly, providing more time to focus on refining the results.

Click here for a technical paper on fault detection using Paradise

DL Fault Detection

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 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.

Click here for a technical paper on seismic facies classification using Paradise

Well Log Visualization

Paradise® brings seismic data to borehole and lithofacies resoltuion in feet/meters. Use the new Well Log Visualization capability in version 3.4 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 to corroborate machine learning to ground truth.

The interpreter can customize the Well Log View, by adding tracks and changing the properties of all data within them. This feature is a powerful tool in the process of correlating machine learning results to the reservoir properties determined by conventional well logs.


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.

Petrel Connector

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

Petrel Connector Features

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
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.

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