Blog
The Power of OpendTect's Attribute Engine
- Written by: Hesham Refayee
Seismic attribute analysis has been an integral part of seismic interpretation workflows for decades. We can compute numerous attributes from seismic data and use them to interpret structural, stratigraphical, and DHI anomalies.
Our attribute engine supports the most advanced seismic attributes in our industry. Here are some examples:
Machine Learning Workflows – Quick UVQ Waveform Segmentation
- Written by: Paul de Groot
Today, we start a series of posts about Machine Learning Workflows supported in OpendTect. Each workflow is presented in the form of a short video. We will show that you do not have to be a data science expert to use Machine Learning solutions in day-to-day seismic interpretation work. Some videos show workflows from our original Neural Networks plugin, which is now an integral part of the Machine Learning plugin. Other videos describe new workflows for deep learning algorithms such as CNNs and Unets. Amongst others we will show that labels can be created with a paintbrush (image-to-point applications) and by drawing polygons (image-to-image applications).
Read more: Machine Learning Workflows – Quick UVQ Waveform Segmentation
OpendTect patch release 6.6.10
- Written by: Arnaud Huck
Dear OpendTect Users,
We have made a patch release for our latest stable version: OpendTect 6.6.10, which is now available for installation/update..
XGBoost NPHI Prediction
- Written by: Paul de Groot
In today’s post we show how Machine Learning can be used to predict a missing log. The model is called XGBoost (eXtreme Gradient Boosting). This is an extremely fast ensemble technique in which the overall performance of a base model is improved through boosting.
Finding Hydrocarbons with OpendTect's Fluid Contact Finder and Chimney Cube
- Written by: Paul de Groot
Today, we show an example of how to de-risk an exploration prospect using OpendTect’s Fluid Contact Finder (FCF) and Machine Learning plugins.
FCF is a tool to enhance hydrocarbon-induced seismic amplitude anomalies. The idea is simple: all seismic traces lying on the same (depth-)contour of a hydrocarbon-filled structure are expected to encounter the same hydrocarbon column length. In other words, the seismic response will be affected by hydrocarbon-fill in a similar manner. Stacking all traces lying in the same contour interval thus enhances the hydro-carbon effect (the anomaly) while cancelling all other effects (noise and lithological differences). The result of a FCF analysis is presented in the form of a panel of stacked traces organized in contour intervals. Step-changes in amplitude response indicate changes in fluid-fill. Another useful output of FCF analysis is a volume of FCF stacked traces to display amplitude maps of FCF-enhanced anomalies
Read more: Finding Hydrocarbons with OpendTect's Fluid Contact Finder and Chimney Cube
Carbonate Buildup Interpretation
- Written by: Paul de Groot
In seismic sequence stratigraphic studies, we analyze seismic reflection patterns in the context of a chronostratigraphic framework. In OpendTect, such frameworks are defined by a HorizonCube, which consists of a dense set of horizons representing Relative Geologic Time. The SSIS (Sequence Stratigraphic Interpretation System) plugin is an add-on that uses HorizonCube input to decompose depositional systems over geologic time.
Seismic Interpretation Services
- Written by: Paul de Groot
Next to developing OpendTect we also offer advanced seismic interpretation services. Our experts have worked on data sets from around the globe. We have extensive experience in hydrocarbon exploration, field development, time-lapse monitoring, unconventional plays and geothermal studies.
OpendTect patch release 6.6.9
- Written by: Arnaud Huck
Dear OpendTect Users,
We have made a patch release for our latest stable version: OpendTect 6.6.9, which is now available for installation/update. We are proud to announce that this release is the first one fully certified by Red Hat.
Improving interpretability with Machine Learning
- Written by: Paul de Groot
Today, we show another great example of how you can quickly improve the interpretability of seismic data by applying a trained Machine Learning model.
DeSmile is one the models from Lundin-GeoLab (nowadays AkerBP) that is available in OpendTect’s growing library of trained ML models