Machine Learning

Examples and PowerPoint from the webinar of 22nd of April 2021 by David Markus: develop your own Machine Learning tools and workflows with OpendTect

Jupyter Notebook and images from the webinar of 29th of April 2021 by Olawale Ibrahim: how to prepare well logs to get optimal Machine Learning results

Jupyter Notebook from the webinar of 22nd of September 2021 by Friso Brouwer: how to extract data from OpendTect into a Python environment by coding in Jupyter Notebook

Jupyter Notebook from the webinar of 17th of February 2022 by Olawale Ibrahim & Sergey Tsimfer: Porting Machine Learning horizon tracking Notebook to OpendTect

Jupyter Notebooks and Python script from the webinar of 15th of June 2023 by Hadyan Pratama: Building and Training your 2D CNN Model with OpendTect

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

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

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

de Groot, P., Pelissier, M., Refayee, H., and van Hout, M. [2021]. Deep Learning Seismic Object Detection Examples. DEW Journal, July 2021

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

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

Refayee, H., and Hemstra, N. [2019]. The Use of Machine Learning to Enhance Faults and Fractures Detection in Seismic Data. 1st Applied Geoscience Conference

Rimaila, K. [2019]. Interpretation of Hydrocarbon Migration Pathways Using Latest Developments in Machine Learning - Green Canyon, Gulf of Mexico. GeoGulf (GCAGS)

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

Rimaila, K., Mustaqeem, A. and Baranova, V. [2015]. Neural Network Application of Curvature Attribute for Fracture Analysis. GeoConvention 2015: New Horizons

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

Brouwer, F.C.G., Connolly, D. and Tingdahl, K. [2011]. A Guide to the Practical Use of Neural Networks.

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

Aminzadeh, F. and De Groot, P. [2005]. A neural networks based seismic object detection technique. SEG Technical Program, p.775-778

Aminzadeh, F., Ross, C. and Brouwer, F. [2005]. Assessing hydrocarbon risk with neural network classification methods. EAGE 67th Conference & Exhibition Madrid

Aminzadeh, F. and De Groot, P. [2004]. Neural network applications. Soft computing for qualitative and quantitative seismic object detection and reservoir property prediction. First Break

De Groot, P., Ligtenberg, H., Oldenziel, T., Connolly, D. and Meldahl, P. [2004]. Examples of multi-attribute, neural network-based seismic object detection. 3D Seismic Technology, GS Memoir No. 29

Heggland, R. [2004]. Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis. AAPG Bulletin, Volume 88, No. 6

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

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, Extended abstract

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

Ligtenberg, H. and Wansink, G. [2002]. Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia. Developments in Petroleum Science, Volume 51, Chapter 19, p.397

Ligtenberg, H. and Wansink, G. [2001]. Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia. Journal of Petroleum Geology JPG, vol.24(4), p.389

Wansink, G., Yang, L., et al. [2001]. A new confidence bound estimation method for neural networks, an application example. 63rd EAGE conference, Amsterdam

Aminzadeh, F., et al. [2000]. Reservoir parameter estimation using a hybrid neural network. Computer and Geoscience

De Groot, P. and Bril, A. [2000]. dGB-GDI Concepts & theory.

Heggland, R., Meldahl, P., Bril, A. and De Groot, P. [2000]. Detection of Seismic Chimneys by neural networks, a New Prospect Evaluation Tool. 62nd EAGE conference, Glasgow

Meldahl, P., Heggland, R., 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

Oldenziel, T., De Groot, P. and Kvamme, L. [2000]. Neural network-based prediction of porosity and water saturation from time-lapse seismic; a case study. First Break

Yang, L., et al. [2000]. An evaluation of confidence bound estimation methods for neural networks. ESIT

De Groot, P. [1999]. Seismic Reservoir Characterisation Using Artificial Neural Networks. 19th Mintrop seminar, Muenster, Germany

De Groot, P. [1999]. Volume Transformation by way of Neural Network Mapping. 61st EAGE Conference, Helsinki

Meldahl, P., Heggland, R., 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

Meldahl, P., Heggland, R., 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

El Oul, J. [1998]. Neural networks introduction.

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

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