Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
A cutting-edge and adaptable data management system that significantly improves collaboration, security, and operational efficiency. This lightweight architectural framework is designed to accommodate numerous users and diverse projects seamlessly. With a centralized repository that adheres to the strictest security protocols, it offers customizable features based on defined user roles and permissions. Teams working in seismic analysis, petrophysics, and geological modeling can collaborate effectively through real-time data workflows and shared access, all within a unified visualization setting. The system is equipped with import/export capabilities that support a variety of data formats, facilitating seamless interaction with the OSDU Data Platform and external repositories. Moreover, the integration of common data models, standardized interfaces, and visualization tools enhances the overall user experience. Its scalable nature ensures that it can adapt to evolving performance and storage requirements as data volumes increase over time, allowing organizations to keep pace with their growing needs. In this way, the infrastructure not only meets current demands but also anticipates future challenges in data management.
Description
Paradise employs advanced unsupervised machine learning alongside supervised deep learning techniques to enhance data interpretation and derive deeper insights. It creates specific attributes that help in extracting significant geological information, which can then be utilized for machine learning analyses. The system identifies attributes that exhibit the most variation and influence within a geological context. Additionally, it visualizes neural classes and their corresponding colors from Stratigraphic Analysis, which reveal the spatial distribution of different facies. Faults are detected automatically through a combination of deep learning and machine learning methods. Furthermore, it allows for a comparison between machine learning classification outcomes and other seismic attributes against traditional high-quality logs. Lastly, it generates both geometric and spectral decomposition attributes across a cluster of computing nodes, achieving results in a fraction of the time it would take on a single machine. This efficiency enhances the overall productivity of geoscientific research and analysis.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
AspenTech
Country
United States
Website
www.aspentech.com/en/products/sse/aspen-epos
Vendor Details
Company Name
Geophysical Insights
Founded
2009
Country
United States
Website
www.geoinsights.com/products/
Product Features
Oil and Gas
Compliance Management
Equipment Management
Inventory Management
Job Costing
Logistics Management
Maintenance Management
Material Management
Project Management
Resource Management
Scheduling
Work Order Management
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization