dGB Earth Sciences


Thursday 15 June 4 pm CET. Register here: https://attendee.gotowebinar.com/register/7055758961598683735

Join us for an exclusive webinar series featuring two back-to-back sessions that will provide a comprehensive overview of training a 2D CNN model architecture and seamlessly importing it into the OpendTect environment.

Dear OpendTect Users,

We have made a new major release of our software: OpendTect 7.0.0, which is now available for installation/update.

Paul de Groot, Arnaud Huck and I will be at the EAGE in Vienna next week from 5th to 8th June - Booth 4314.

We will be sharing exciting updates about our upcoming major release: OpendTect version 7, scheduled to be released directly after the EAGE.

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.

Thursday, May 25th at 4 pm CET

Explore the diverse approaches to handling noisy or problematic seismic data in OpendTect. From trusted attributes to cutting-edge Machine Learning techniques.

Exciting News! dGB Earth Sciences Partners with BLIX Consultancy & GEO2 Engineering B.V. to provide expert support for Doordewind Wind Farm Zone

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.

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.

Free webinar, Thursday 20 April 4 PM CET

Join us for an exclusive webinar as we unveil our latest innovative commercial plugin – the "Surface Segments" plugin

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.