Machine Learning Publications

Markus, D., Rimaila, K., de Groot, P. and Muammar, R. 'Pseudo3D', A Post-Stack Approach to Transforming 2D Seismic into 3D. First Break 43(2):59-62, February 2025.

Aminzadeh, F. Leveraging AI Requires Finesse, But Delivers Huge Rewards The American Oil & Gas Reporter, Editor's Choice November 2024

Araque-Pérez, C.J., Teixidó, T., de Lis Mancilla, F., Morales, J. Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain Tectonophysics, July 2024

Pratama, H., Oke, M., Mogg, W., Markus, D., Huck, A., & de Groot, P. Lessons learnt for tuning a Machine Learning fault prediction model First Break, February 2024

de Groot, M., Refayee, H., and van Hout, M. How Machine Learning saves time and costs in subsurface interpretation studies DEW Journal, February 2023

de Groot, P., and Refayee, H. How reusing trained machine learning models accelerates and improves the work of operational geoscientists. First Break, February 2023

Dixit, A., Mandal, A., Ganguli, S. S., & Sanyal, S. Genetic-evolutionary adaptive moment estimation-based semisupervised deep sequential convolution network for seismic impedance inversion: Application and uncertainty analysis. Geophysics, 88(2), R225-R242.

Farfour, M., Foster, D. J. Machine learning and seismic attributes for prospect identification and risking: an example from Offshore Australia Second International Meeting for Applied Geoscience & Energy 2022

Brouwer, F., De Groot, P. 3D seismic facies segmentation using supervised and unsupervised learning approaches Denver Geophysical Society, ML/AI Workshop, 20 Oct. 2022

De Groot, P., Huck, A. and M. van Hout Reconstructing seismic images and creating pseudo-3D volumes: a machine learning approach First Break, volume 40, February 2022

De Groot, P. and Huck, A. Machine Learning workflows to create pseudo-3D from 2D seismic. SEG Digital Intelligence Series, 2nd edition, 30 Nov. – 2 Dec. 2021

de Groot, P. and van Hout, M. Filling gaps, replacing bad data zones and super-sampling of 3D seismic volumes through Machine Learning. EAGE 2021 Annual Conference, October 2021

Jaglan, H., Kocsis, G., Lakhliffi, A., and de Groot, P. Experiences with Machine Learning and Deep Learning Algorithms for Seismic, Wells and Seismic-to-Well Applications. EAGE 2021 Annual Conference, October 2021

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

de Groot, P., Pelissier, M., and van Hout, M. Seismic classification: A Thalweg tracking/machine learning approach. First Break, Vol. 39, pg. 59-64, March 2021.

Saadat, M., Hashemi, H., Nabi-Bidhendi, M. and de Groot, P. 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

Shady A. Negm, Mohamed H. Khalil & Ali Bakr Gas prediction through the LMR method using post-stack inversion and multi-attributes, F3 cube, North Sea, Netherlands Volume 13, article number 674, July 2020

Dixit, A., & Mandal, A. Detection of gas chimney and its linkage with deep-seated reservoir in Poseidon, NW shelf, Australia from 3D seismic data using multi-attribute analysis and artificial neural network approach. Journal of Natural Gas Science and Engineering, 83, 103586.

Dixit, A., A. Mandal, C. P. Kumar. Detection of natural gas leakage from deep-seated reservoir using multi attribute analysis through artificial neural network in Poseidon basin, North-West shelf Australia: Society of Petroleum Geophysicists (SPG), 13th Biennial International Conference & Exposition, February 23-25, 2020, Kochi, India.

Kocsis, G. and Jaglan, H. 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. 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. The Use of Machine Learning to Enhance Faults and Fractures Detection in Seismic Data. 1st Applied Geoscience Conference

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

Kumar, P. C., & Mandal, A. Enhancement of fault interpretation using multi-attribute analysis and artificial neural network (ANN) approach: A case study from Taranaki Basin, New Zealand. Exploration Geophysics, 49(3), 409-424.

Baranova, B., Mustaqeem, A., et al. Cardium Formation Paleo-Depositional Facies Analyses Using Stochastic Inversion and Seismic Stratigraphy. GeoConvention 2016, Calgary, Canada.

Connolly, D. and De Groot, P. Chimney Atlas to Quantify Top Seal and Charge Risk: Case Study from Maari Oil Field, Taranaki Basin, New Zealand. EAGE 78th Conference & Exhibition, Vienna.

Singh, D., Kumar, P.C. and Sain, K. 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.

Singh, D., Kumar, P.C. and Sain, K. Interpretation of gas chimney in the Maari 3D field of southern Taranaki Basin, New Zealand SEG Technical Program

Connolly, D. Atlas of gas chimney occurrences associated with oil and gas fields and dry holes: Case studies from deepwater Gulf of Mexico. SEG Technical Program, Expanded Abstracts, p. 1668-1672.

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

Connolly, D., Kemper, J. and Thomas, I. Using gas chimney detection to assess hydrocarbon charge and top seal effectiveness - offshore, Namibia 76th EAGE Conference & Exhibition

Eichkitz, C.G., De Groot, P. and Brouwer, F. Visualizing anisotropy in seismic facies using stratigraphically constrained, multi-directional texture attribute analysis AAPG Hedberg Conference

Huck, A., and Hicks, G. Using the 4D Hit Cube for seismic inversion in the North Sea's Blake Field. World Oil.

Brouwer, F., Huck, A., Hemstra, N. and Braga, I. Extracting full-resolution models from seismic data to minimize systematic errors in inversion: Method and examples. The Leading Edge, p. 546-554.

Jones, C.E., Edgar, J.A., Selvage, J.I., and Crook, H. Building Complex Synthetic Models to Evaluate Acquisition Geometries and Velocity Inversion Technologies BG Group

Rahimi Zeynal, A., Aminzadeh, F. Clifford, A. Combining Absorption and AVO Seismic Attributes Using Neural Networks to High-Grade Gas Prospects SPE Western Regional Meeting

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

Huck, A., De Groot, P., Manning, T. and Rietveld, W. Neural-network based multi-azimuth processing 72nd EAGE Conference & Exhibition

Huck, A., Quiquerez, G. and De Groot, P. Improving Seismic Inversion through Detailed Low Frequency Model Building 72nd EAGE Conference & Exhibition

Connolly, D., Aminzadeh, F., Selva, C. and Curia, D. Using Integrated Gas Chimney Processing, Frequency Attenuation attributes, and Seismic Facies Classification to Delineate Oil Filled Reservoirs: Case studies from the Oriente Basin, Ecuador. Top 10 Poster Presentation AAPG convention.

Hashemi, H., Tax, D.M.J., et al. Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier. Nonlinear Processes in Geophysics, Volume 15, 863-871.

Ayeni, G.O., Huck, A. and De Groot, P. Extending reservoir property prediction with pseudo-wells First Break

Connolly, D.L, Brouwer, F. and Walraven, D. Detecting Fault-Related Hydrocarbon Migration Pathways in Seismic Data: Implications for Fault-Seal, Presure, and Charge Prediction

Flierman, W., van der Weide, J.G., Wever, A., Brouwer,F. and Huck., A. Use of spatial, frequency and curvature attributes for reservoir, fluid and contact predictions SEG Las Vegas Annual Meeting

Welsh, A., Connolly, D.L., Selva, C., Curia, D. and Huck, A. High Frequency Attenuation and Low frequency shadows in Seismic Data Caused by Gas Chimneys, Onshore Ecuador 70th EAGE Conference & Exhibition

Zhao, B., Brouwer, F.G.C., Aminzadeh, F., Morris, S. and Harris, R. Multiple prediction and reservoir characterization of a tight sand reservoir SEG Las Vegas Annual Meeting

Ayeni, G.O. and Huck, A. The Hit Cube: a stochastic pseudo well matching tool for seismic prediction of rock and fluid properties AAPG Annual Convention and Exhibition

Ayeni, G.O., Huck, A. and De Groot, P. The HIT Cube; Matching Monte Carlo Simulated Pseudo-Wells to Seismic Data for Predictions with Uncertainties 69th EAGE Conference & Exhibition

Aminzadeh, F. and Brouwer, F. Integrating neural networks and fuzzy logic for improved reservoir property prediction and prospect ranking 76th SEG Annual Meeting

Klutts, J., Connolly, D.L., Aminzadeh, F. and Brouwer, F. Visualizing Gas Chimney Volumes Reduces Exploration Risk: A Case Study from Onshore Louisiana 76th SEG Annual Meeting

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

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

Heggland, R. Using gas chimneys in seal integrity analysis: A discussion based on case histories Evaluating fault and cap rock seals

Walraven, D., Connolly, D.L. and Aminzadeh, F. Determining Migration Pathway in Marco Polo Field Using Chimney Technology EAGE 67th Conference & Exhibition Madrid

De Groot, P., Ligtenberg, H., Oldenziel, T., Connolly, D. and Meldahl, P. (Statoil) Examples of multi-attribute, neural network-based seismic object detection. In: 3D Seismic Technology, GS Memoir No. 29.

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

Evolutionary computing and other aspects of soft computing Soft computing for qualitative and quantitative seismic object detection and reservoir property prediction

Aminzadeh, F. and Connolly, D. Hydrocarbon Phase Detection and Other Applications of Chimney Technology AAPG Int. Conference

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

Aminzadeh, F. and Wilkinson, D. Fuzzy logic applications Soft computing for qualitative and quantitative seismic object detection and reservoir property prediction

Heggland, R. Hydrocarbon Migration and Accumulation Above Salt Domes - Risking of Prospects by the Use of Gas Chimneys Salt-Sediment Interactions and Hydrocarbon Prospectivity: Concepts, Applications and Case Studies for the 21st Century

Ligtenberg, H. Sealing quality analysis of faults and formations by means of seismic attributes and neural networks EAGE Proceedings of Fault and Top Seals conference

Walraven, D., Aminzadeh, F. and Connolly, D. Predicting seal risk and charge capacity using chimney processing: Three Gulf of Mexico case histories SEG National Convention

Ligtenberg, H. 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.

Alvarado, J., Aminzadeh, F. and Connolly, D. Application of gas chimney technology in the Lamprea area, offshore GOM SEG annual meeting

Connolly, D. L. and Aminzadeh, F. Geo-Hazard Detection with Chimney Cubes Offshore Technology Conference

Heggland, R. (Statoil). Application of Chimney Cubes in the Design of Geochemical Surveys AAPG conference

Ligtenberg, H. Sealing quality analysis of faults and formations by means of seismic attributes and neural networks EAGE Fault and Top Seal conference

Ligtenberg, H. and Connolly, D. Chimney detection and interpretation - revealing sealing quality of faults, geohazards, charge of and leakage from reservoirs Migration and interaction in sedimentary basins and orogenic belts

Aminzadeh, F., Connolly, D., Heggland, R. (Statoil), Meldahl, P. (Statoil) and De Groot, P. Geohazard detection and other applications of chimney cubes. The Leading Edge, p.681-685.

Aminzadeh, F. and Connolly, D. Looking for gas chimneys and faults AAPG Explorer

Aminzadeh, F., De Groot, P., Berge, T. (Forest Oil), Oldenziel, T. and Ligtenberg, H. Determining migration path from seismically derived gas chimneys AAPG Hedberg Conference

Berge, T.B., Aminzadeh, F., De Groot, P. and Oldenziel, T. Seismic inversion successfully predicts reservoir, porosity, and gas content in Ibhubesi Field, Orange Basin, South Africa The Leading Edge

Heggland, R. (Statoil). Seismic evidence of vertical fluid migration through faults, Applications of Chimney and Fault detection AAPG Hedberg Conference

Ligtenberg, H. and Wansink, G. (formerly dGB). Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Soft computing and intelligent data analysis in oil exploration

Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, A. and De Groot, P. Identifying faults and gas chimney using multiattributes and neural networks. The Leading Edge.

Aminzadeh, F., De Groot, P., Berge, T. (Forest Oil) and Valenti, G. (AGIP) Using gas chimneys as an exploration tool, Part 1 and Part 2. World Oil.

Grennberg Fismen, B., Clausen, S., Yang, L., Carlin, M., Kavli, T. (all Sintef) and Wansink, G. (formerly dGB). Unsupervised segmentation in seismic data analysis ICIP conference

Ligtenberg, H. Examples of seismic chimney detection and interpretation Subsurface sediment mobilisation conference

Ligtenberg, H. and Wansink, G. (formerly dGB). Neural network prediction of permeability in El Garia Formation, Ashtart oilfield, offshore Tunesia Journal of Petroleum Geology JPG

–Tingdahl, M.K., Bril, A.H. and De Groot, P.F.M. Improving seismic chimney detection using directional attributes Journal of Petroleum Science and Engineering

Tingdahl, K., De Groot, P. and Heggland, R. (Statoil). Semi-automated object detection in 3-D seismic data Offshore

Wansink, G. (formerly dGB), Yang, L. (Sintef), et al. A new confidence bound estimation method for neural networks, an application example 63rd EAGE conference

Heggland, R. (Statoil), Meldahl, P. (Statoil), De Groot, P. and Aminzadeh, F. Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico. American Oil & Gas Reporter.

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

Heggland, R. (Statoil), Meldahl, P. (Statoil), Bril, A. and De Groot, P. Detection of Seismic Chimneys by neural networks, a New Prospect Evaluation Tool 62nd EAGE conference

Meldahl, P. (Statoil), Heggland, R. (Statoil), Bril, B. and De Groot, P. Semi-automated detection of seismic objects by directive attributes and neural networks, method and applications 62nd EAGE conference

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

Oldenziel, T., De Groot, P. and Kvamme, L. (formerly Statoil). Prediction of static and dynamic parameters from time-lapse 3-D seismic 62st EAGE Conference

Yang, L. (Sintef), et al. An evaluation of confidence bound estimation methods for neural networks ESIT

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

Meldahl, P. (Statoil), Heggland, R. (Statoil), De Groot, P. and Bril, A. 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. (Statoil), Heggland, R. (Statoil), De Groot, P. and Bril, A. 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.

De Groot, P. Seismic Reservoir Characterisation Using Artificial Neural Networks 19th Mintrop seminar

Bril, A. and De Groot, P. Stochastic inversion of seismic data by evaluation of pseudo-wells. 60th EAGE conference, Leipzig.

El Oul, J. Neural networks introduction

Braunschweig, B., Bremdal, B.A. and De Groot, P. Neural Network experiments on synthetic seismic data. Artificial Intelligence in the Petroleum Industry, p. 93-124.

De Groot, P.F.M. Total Space Inversion: Concept and Experiments in Model Space 57th EAGE Conference

De Groot, P.F.M., Campbell, A.E., Kavli, T. and Melnyk, D. Reservoir characterization from 3D seismic data using artificial neural networks and stochastic modelling techniques. 55th EAGE Conference, Stavanger.