Join the OpendTect ML Discord communityOpendTect Machine Learning - Knowledge Base
The webinar videos: Machine Learning and OpendTect: Building and Training your 2D CNN Model and Applying and Fine-tuning your Trained Model: From U-Net Architecture to Real Seismic Data Interpretation are now online!
Getting started with Machine Learning in OpendTect
To get started with Machine Learning in OpendTect several datasets are provided on TerraNubis with wich all plugins are available for all users. There is of course F3 offshore the Netherlands, Penobscot and recently two more sets were added in support of FORCE competition, they can be found on TerraNubis.
Follow the steps below to install OpendTect 7.0 and download the complete datasets.
- Step 1: Download the installer for Opendtect 7.0
- Step 2: Download the free datasets on TerraNubis
OpendTect Machine Learning Developers' Community on Discord
Join the OpendTect Machine Learning Developers' Community on Discord.
For more information on how to become a member and be part of the Community please read the FAQ.
Videos
- Watch the Machine Learning Webinar series of dGB Earth Sciences
It will show the steps needed to make and train your own models, shows you workflows, goes over some of the basics and theory and help you with Machine Learning development: - Machine Learning Webinars: Part 1: Theory: Introduction
- Machine Learning Webinars: Part 2: Theory: Deep Neural Networks and Other New Algorithms
- Machine Learning Webinars: Part 3: Applications: Seismic, Logs, and, Seismic-to-log
- Machine Learning Webinars: Part 4: Synthesizing Training Data with SynthRock
- Machine Learning Webinars: Part 5: Creating and Adding New Models
- Machine Learning Webinar Q&A - Demo Image to Image workflow
- Machine Learning Webinar Q&A - Demo Log-Log Prediction workflow
- Machine Learning Webinar Q&A - Demo Seismic Object Detection workflow
- OpendTect Webinar: Machine Learning workflows for seismic data interpolation
- OpendTect Webinar: Machine Learning Applications for Seismic Interpretation
- OpendTect Webinar: Seismic Classification: a Thalweg Tracker / Machine Learning Approach
- OpendTect Webinar: Develop your own Machine Learning tools and workflows with OpendTect
- OpendTect Webinar: How to prepare well logs to get optimal Machine Learning results
- OpendTect Webinar: OpendTect's Hybrid Machine Learning Solution
- Image '21 Master Class Webinar: Log-log Prediction Using Machine Learning, Seismic Classification a Thalweg Tracker & Machine Learning Approach
- Image '21 Master Class Webinar: How to extract data from OpendTect into a Python environment by coding in Jupyter Notebook
- OpendTect Machine Learning Developers Q&A Webinar: how can I add my own trained model and other Q+A
- OpendTect Machine Learning Developers Q&A Webinar: how to use my own Keras model in the ML UI?
- OpendTect Webinar: Porting Machine Learning horizon tracking Notebook to OpendTect
- OpendTect Webinar: Accelerating the Time from Machine Learning R&D to Deployment
- OpendTect Webinar: Thalweg tracker
- OpendTect Webinar: Cleaning up your data - Dip Steering and other filters
- OpendTect Webinar: Machine Learning with SynthRock link
- OpendTect Webinar: Data Conditioning
- OpendTect Webinar: Machine Learning and OpendTect: Building and Training your 2D CNN Model
- OpendTect Webinar: Fine-tuning your Trained Model: From U-Net Architecture to Real Seismic Data Interpretation
Blogs
- 14 April 2022: Fault Dip and Azimuth for Machine Learning Fault Likelihood
- 21 April 2022: Integrated Machine Learning Rock Property Prediction Workflow
- 02 June 2022: Sharing Trained Machine Learning Models will redefine our modus operandi
- 07 July 2022: PyTorch added as development environment in OpendTect
- 14 July 2022: Sharing Trained Machine Learning Models is Redefining our Modus Operandi
- 21 July 2022: Desmile; another pearl in OpendTect’s library of pre-trained Machine Learning models
- 28 July 2022: AJAX, an ML model to enhance the visual quality and interpretability of 3D seismic
- 04 August 2022: Machine Learning: Lundin GeoLab SimpleDenoise
- 27 October 2022: Improving interpretability with Machine Learning
- 23 February 2023: XGBoost NPHI Prediction
- 02 March 2023: Machine Learning Workflows – Quick UVQ Waveform Segmentation
- 23 March 2023: Machine Learning Workflows – Using AI for Salt Detection
- 30 March 2023: Machine Learning Workflows - Ready to go AI workflows
- 06 April 2023: Machine Learning Workflows - Fast and Simple Seismic Facies Analysis - 3D UVQ Waveform Segmentation
Documentation
User documentation
System Requirements
Development
Workflows
- Machine Learning Workflow: Wells Log-Log Prediction (Density)
- Machine Learning Workflow: Wells Lithology Classification
- Machine Learning Workflow: Seismic Classification (Supervised 3D)
- Machine Learning Workflow: Seismic Unet 3D Fault Predictor
- Machine Learning Workflow: 3D Seismic + Wells Rock Property Prediction
- Machine Learning Workflow: Seismic Image to Image Faults Prediction
- Machine Learning Workflow: Seismic Image Regression Unet Fill Seismic Traces
- Machine Learning Workflow: Seismic Lundin Smoother
Machine Learning Workflow Blogs
| Publish Date | Author | Title | Resources |
|---|---|---|---|
| 02 March 2023 | Paul de Groot | Machine Learning Workflows – Quick UVQ Waveform Segmentation | Video |
| 23 March 2023 | Paul de Groot | Machine Learning Workflows - Using AI for Salt Detection | Video |
| 30 March 2023 | Marieke van Hout | Machine Learning Workflows - Ready to go AI workflows - Apply Pre-trained Model | Video |
| 06 April 2023 | Paul de Groot | Machine Learning Workflows - Fast and Simple Seismic Facies Analysis - 3D UVQ Waveform Segmentation | Video |
| 20 April 2023 | Paul de Groot | Machine Learning Workflows - De-risking charge and seal issues with AI - Neural Network Chimney Cube | Video |
| 04 May 2023 | Paul de Groot | Machine Learning Workflows - Seismic Inversion using AI - Machine Driven Seismic Inversion Workflow | Video |
| 25 May 2023 | Paul de Groot | Machine Learning Workflows - Supervised AI Seismic Facies | Video |
Published Articles
de Groot, P. and van Hout, M., [2021] Filling gaps, replacing bad data zones and super-sampling of 3D seismic volumes through Machine Learning. EAGE 2021 Annual Conference, Oral presentation in Digitalization & AI: Seismic Data Processing I, Tuesday, 19 October 2021 at 14:45.
Jaglan, H., Kocsis, G., Lakhliffi, A., and de Groot, P. [2021] Experiences with Machine Learning and Deep Learning Algorithms for Seismic, Wells and Seismic-to-Well Applications. EAGE 2021 Annual Conference, Oral presentation in Digitalization & AI: Reservoir and Wells, Thursday, October 21, 2021 at 15:55.
Saadat, M., Hashemi, H., Nabi-Bidhendi, M. and de Groot, P., [2021] Incorporating acquisition geometry in deep learning-based full waveform inversion. EAGE 2021 Annual Conference, E-Poster: FWI and Velocity Analysis. Geophysics 2, Thursday, October 21, 2021, 8:30 AM - 11:30 AM
de Groot, P., Pelissier, M., Refayee, H., and van Hout, M., [2021]. Deep Learning Seismic Object Detection Examples. DEW Journal, July 2021
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de Groot, P., Pelissier, M., and van Hout, M., [2021]. Seismic classification: A Thalweg tracking/machine learning approach. First Break, Vol. 39, pg. 59-64, March 2021.
Gogia, R., Singh, R., de Groot, P., Gupta, H., Srirangarajan, S., Phirani, J. and Ranu, S. [2020]. Tracking 3D Horizons with a New, Hybrid Tracking Algorithm. Interpretation Journal, Nov. 2020.
Kocsis, G. and Jaglan, H. [2019]. Pseudo-Wells based HitCube 'trace-matching' and Machine Learning Inversions: Seismic Reservoir Characterization in a Challenging Environment. EAGE Subsurface Intelligence Workshop, Bahrain.
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Kumar, P. C., Sain, K., and Mandal, A. [2019]. Delineation of a buried volcanic system in Kora prospect off New Zealand
using artificial neural networks and its implications. Journal of Applied Geophysics 161, p. 56 - 75
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Refayee, H., and Hemstra, N., [2019]. The Use of Machine Learning to Enhance Faults and Fractures Detection in Seismic Data. 1st Applied Geoscience Conference
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Rimaila, K., [2019]. Interpretation of Hydrocarbon Migration Pathways Using Latest Developments in Machine Learning - Green Canyon, Gulf of Mexico. GeoGulf (GCAGS; Gulf Coast Association of Geological Societies)
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Singh, D., Kumar, P.C. and Sain, K. [2016]. Interpretation of gas chimney from seismic data using artificial neural network: A study from Maari 3D prospect in the Taranaki basin, New Zealand. Journal of Natural Gas Science and Engineering.
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Rimaila, K., Mustaqeem, A. and Baranova, V. [2015]. Neural Network Application of Curvature Attribute for Fracture Analysis. GeoConvention 2015: New Horizons.
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Rahimi Zeynal, A., Aminzadeh, F. Clifford, A. [2012]. Combining Absorption and AVO Seismic Attributes Using Neural Networks to High-Grade Gas Prospects. SPE Western Regional Meeting, Bakersfield, California.
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Brouwer, F.C.G., Connolly, D. and Tingdahl, K. [2011]. A Guide to the Practical Use of Neural Networks.
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Hashemi, H., Tax, D.M.J., Duin, R.P.W., Javaherian, A. and De Groot, P. [2008]. Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier. Nonlinear Processes in Geophysics, Volume 15, 863-871.
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Aminzadeh, F. and De Groot, P. [2005]. A neural networks based seismic object detection technique. SEG Technical Program, Expanded Abstracts, p.775-778.
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Aminzadeh, F., Ross, C. and Brouwer, F. [2005]. Assessing hydrocarbon risk with neural network classification methods. EAGE 67th Conference & Exhibition Madrid, Spain.
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Aminzadeh, F. and De Groot, P. [2004]. Neural network applications. In: Aminzadeh, F., De Groot, P. and Wilkinson, D. (Eds.) Soft computing for qualitative and quantitative seismic object detection and reservoir property prediction. First Break.
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De Groot, P., Ligtenberg, H., Oldenziel, T., Connolly, D. and Meldahl, P. (Statoil). [2004]. Examples of multi-attribute, neural network-based seismic object detection. In: Davies, R.J., Cartwright, J.A., Stewart, S.A, Lappin, M. and Underhill, J.R. (Eds.) 3D Seismic Technology; Application to the Exploration of Sedimentary Basins. GS Memoir No. 29.
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Heggland, R. [2004]. Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis. AAPG Bulletin, Special Theme Issue: High-resolution studies of continental margin geology and geohazards, Volume 88, No. 6.
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Ligtenberg, H. [2004]. Sealing quality analysis of faults and formations by means of seismic attributes and neural networks. EAGE Proceedings of Fault and Top Seals conference, Montpellier.
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Ligtenberg, H. [2003]. Sealing quality analysis of faults and formations by means of seismic attributes and neural networks. EAGE Fault and Top Seal conference, Montpellier, France, Extended abstract.
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Ligtenberg, H. [2003]. Unravelling the petroleum system by enhancing fluid migration paths in seismic data using a neural network based pattern recognition technique. Geofluids magazine, 3, p.255-261.
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Ligtenberg, H. and Wansink, G. (formerly dGB). [2002]. Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia. In: Nikravesh, M., Aminzadeh, F. and Zadeh, L.A. (Eds.) Soft computing and intelligent data analysis in oil exploration. Developments in Petroleum Science, Volume 51, Chapter 19, p.397.
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Ligtenberg, H. and Wansink, G. (formerly dGB). [2001]. Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia. Journal of Petroleum Geology JPG, vol.24(4), p.389.
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Wansink, G. (formerly dGB), Yang, L. (Sintef), et al. [2001]. A new confidence bound estimation method for neural networks, an application example. 63rd EAGE conference, Amsterdam.
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Aminzadeh, F., et al. [2000]. Reservoir parameter estimation using a hybrid neural network. Computer and Geoscience.
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De Groot, P. and Bril, A. [2000]. dGB-GDI Concepts & theory.
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Heggland, R. (Statoil), Meldahl, P. (Statoil), Bril, A. and De Groot, P. [2000]. Detection of Seismic Chimneys by neural networks, a New Prospect Evaluation Tool. 62nd EAGE conference, Glasgow.
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Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, B. and De Groot, P. [2000]. Semi-automated detection of seismic objects by directive attributes and neural networks, method and applications. 62nd EAGE conference, Extended abstract, Glasgow.
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Oldenziel, T., De Groot, P. and Kvamme, L. (formerly Statoil). [2000]. Neural network-based prediction of porosity and water saturation from time-lapse seismic; a case study. First Break.
Yang, L. (Sintef), et al. [2000]. An evaluation of confidence bound estimation methods for neural networks. ESIT.
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De Groot, P. [1999]. Seismic Reservoir Characterisation Using Artificial Neural Networks. 19th Mintrop seminar, Muenster, Germany.
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De Groot, P. [1999]. Volume Transformation by way of Neural Network Mapping. 61st EAGE Conference, Helsinki.
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Meldahl, P. (Statoil), Heggland, R. (Statoil), De Groot, P. and Bril, A. [1999]. The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 1; Methodology. 69th SEG conference, Houston.
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Meldahl, P. (Statoil), Heggland, R. (Statoil), De Groot, P. and Bril, A. [1999]. The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part 2; Interpretation. 69th SEG conference, Houston.
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El Oul, J. [1998]. Neural networks introduction.
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Braunschweig, B., Bremdal, B.A. and De Groot, P. [1996]. Neural Network experiments on synthetic seismic data. Artificial Intelligence in the Petroleum Industry, p. 93 - 124.
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De Groot, P.F.M., Campbell, A.E., Kavli, T. and Melnyk, D. [1993]. Reservoir characterization from 3D seismic data using artificial neural networks and stochastic modelling techniques. 55th EAGE Conference, Stavanger.
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FAQ
GitHub repositories
- dgbpy is a framework for research and deployment of machine learning models from seismic and well data
- odpy is a framework for research and deployment that allows for basic interactions with the OpendTect software and database
- OpendTect-ML-Dev contains examples on how to develop your own Machine Learning tools and workflows. Also it includes examples used in webinars.
Code examples and data
- OpendTect-ML-Dev GitHub repository
Here you can find examples on how to develop your own Machine Learning tools and workflows as presented in the Machine Learning webinar videos. We will keep updating this GitHub repository with relevant content: - Download the examples and PowerPoint used in the webinar of 22nd of April 2021 by David Markus that was about develop your own Machine Learning tools and workflows with OpendTect
- Download the Jupyter Notebook and images used in the webinar of 29nd of April 2021 by FORCE 2020 Machine Learning winner Olawale Ibrahim that was about how to prepare well logs to get optimal Machine Learning results.
- Download the Jupyter Notebook used in the webinar of 22nd of September 2021 by Friso Brouwer that was about how to extract data from OpendTect into a Python environment by coding in Jupyter Notebook.
- Download the Jupyter Notebook used in the webinar of 17th of February 2022 by Olawale Ibrahim, dGB Earth Sciences and Sergey Tsimfer, GazProm Neft. The webinar was about Porting Machine Learning horizon tracking Notebook to OpendTect.
- Download the Jupyter Notebooks and Python script used in the webinar of 15th of June 2023 by Hadyan Pratama, dGB Earth Sciences. The webinar was about Building and Training your 2D CNN Model with OpendTect.