Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The process of advanced velocity determination combined with 3D and 2D velocity model construction results in precise and high-quality seismic images that are interpretable in both time and depth. An extensive range of interactive and batch tools for analyzing anisotropic models effectively addresses a wide array of seismic imaging challenges. By integrating interpretation and modeling solutions, the workflow is optimized, minimizing data loss while adhering to geological constraints. The construction of accurate 3D and 2D models is straightforward, making it feasible regardless of the complexity of the underlying structural geology. Furthermore, the system is designed to operate efficiently with highly parallelized capabilities, accommodating extensive 3D datasets and multi-line 2D datasets, whether on-premises or in cloud environments. Accurate seismic images and reliable depth models play a critical role in hydrocarbon exploration and production efforts. The Aspen GeoDepth velocity determination and modeling system provides an effective means for enhancing seismic imaging, facilitating the integration of various processes such as interpretation, velocity analysis, model construction, and time-to-depth conversion, ultimately leading to better decision-making in exploration projects. This comprehensive approach not only improves the quality of the seismic data but also supports more informed resource management strategies.
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-geodepth
Vendor Details
Company Name
Geophysical Insights
Founded
2009
Country
United States
Website
www.geoinsights.com/products/
Product Features
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization