Functionality, features and workflows
Demystify Complex Chained Attributes with Data Flow Visualization in OpendTect
- Written by: Paul de Groot
OpendTect’s attribute engine stands out due to its ability to compute attributes-from-attributes both on-the-fly and in batch-mode. Such capability lets users craft intricate chained attributes and filters. However, as these chains grow, they become more intricate, making it challenging to decipher their computation process.
Read more: Demystify Complex Chained Attributes with Data Flow Visualization in OpendTect
OpendTect Noise Filters Comparison: Cast your preference
- Written by: Paul de Groot
This week we want to show you an example of the 4th Lundin model 'Lundin_GeoLab_SimpleDenoise". We believe this model is applicable to almost all datasets. Unlike the AJAX model, the SimpleDenoise model preserves the amplitude - frequency content. It only removes random noise.
Read more: OpendTect Noise Filters Comparison: Cast your preference
OpendTect Pro supports two OSDU formats: OpenVDS and OpenZGY
- Written by: Paul de Groot
OpendTect is committed to supporting the OSDUTM data platform as developed by the Open Group OSDUTM Forum. As an active member, we contributed to testing OSDU solutions developed for 3D seismic storage and access. Our work for OSDU resulted in the support of two new 3D seismic formats in OpendTect Pro v7: OpenVDS (Bluware) and OpenZGY (SLB). At the moment we support file-based storage and I/O. In future this will be extended to cloud-based data access.
Read more: OpendTect Pro supports two OSDU formats: OpenVDS and OpenZGY
New enhancement to the Well Log viewer
- Written by: Assia Lakhlifi
The well log display tools in the advanced and standard well manager were improved in the latest version OpendTect V7.0.
Machine Learning Workflows - Supervised AI Seismic Facies
- Written by: Paul de Groot
Previously, in our series on OpendTect Machine Learning workflows, we showed an unsupervised workflow for seismic facies analysis. That workflow clustered seismic waveforms to generate a segmentation volume consisting of 50 different segments enabling detailed interpretation of seismic facies.
Today, we show one of several possible workflows in OpendTect using supervised learning. This workflow uses the Thalweg tracker for labeling target positions. In total 8 different label sets were created representing positive and negative amplitude classes of meandering channels, unconfined channels, splays and floodplains.
Read more: Machine Learning Workflows - Supervised AI Seismic Facies
Machine Learning Workflows - Seismic Inversion using AI - Machine Driven Seismic Inversion Workflow
- Written by: Paul de Groot
Today, in our series on OpendTect Machine Learning workflows, we show a workflow for rock property prediction using real wells.
This workflow has many variations. You can train on real or synthetic seismic data to predict well log properties of interest.
Machine Learning Workflows - De-risking charge and seal issues with AI - Neural Network Chimney Cube
- Written by: Paul de Groot
Today, in our series on Machine Learning Workflows, we go back to the origins of the OpendTect Machine Learning platform. OpendTect started life as a neural network-based seismic pattern recognition and attribute processing system. The primary goal of the original system was to create Chimney Cubes for fluid migration path interpretation. The software was used for geohazard interpretation and for de-risking hydrocarbon charge and seal problems.
The video shows a chimney interpretation study in the Gulf of Mexico by Roar Heggland of Equinor.
Machine Learning Workflows - Fast and Simple Seismic Facies Analysis - 3D UVQ Waveform Segmentation
- Written by: Paul de Groot
OpendTect Machine Learning supports supervised and unsupervised workflows for seismic facies analysis.
Today’s post discusses an unsupervised workflow. The 3D UVQ Waveform Segmentation workflow is the 3D variant of the Quick UVQ workflow that we discussed earlier in this series on OpendTect ML workflows. In Quick UVQ we segment (cluster) seismic waveforms (trace segments) around a mapped horizon into a user-defined number of segments. A typical number of segments in Quick UVQ is 10.
Machine Learning Workflows - Ready to go AI workflows - Apply Pre-trained Model
- Written by: Marieke van Hout
OpendTect’s Machine Learning platform is developed for three types of geo-scientists:
- Operational,
- Experimental and
- Research.
Read more: Machine Learning Workflows - Ready to go AI workflows - Apply Pre-trained Model