Technical Paper

In this paper, we introduce a 3D fault directional skeletonization workflow (Figure 1) that uses the dip magnitude and azimuth of a directional Laplacian of a Gaussian (LoG) enhanced discontinuities image. We begin our paper by using principal-component SOF to suppress the random and steeply dipping coherent noise on the seismic amplitude data. Then, we compute the coherence attribute from the original and filtered seismic amplitude volumes and compare the results. Next, we apply a directional LoG filter resulting in a smooth but somewhat blurred image. Finally, we skeletonize the LoG filtered image perpendicularly to sharpen the locally planar features.

By Marwa Hussein, Robert R. Stewart and Jonny Wu | Published with permission: Interpretation Journal | May 2021 Download PDF Technical PaperReferencesDownload PDFTechnical Paper Table of contents Abstract Introduction Geologic background Data set Method Seismic attribute analysis Interactive seismic attribute interpretation Discussion Conclusion Acknowledgements Data and materials availability Abstract Subtle fault detection plays a vital role …

Which Seismic Attributes are Best for Subtle Fault Detection? Read More »

By Jie Qi, Bin Lyu, Xinming Wu and Kurt Marfurt, | Published with permission: SEG| Oct 2020 Download PDF Technical PaperDownload PDFTechnical Paper Table of contents Summary Introduction The CNN-based fault detection workflow Image processing-based fault detection workflow Field data applications Conclusions Summary Convolutional Neural Networks (CNN)-based fault detection method is an emerging technology that …

Comparing Convolutional Neural Networking and Image Processing Seismic Fault Detection Methods Read More »

By Bob Hardage, Tom Smith, Diana Sava, Yi Wang, Rocky Roden, Gary Jones, and Sarah Stanley | Published with permission: Interpretation Journal | November 2020 Download PDF Technical PaperReferencesDownload PDFTechnical Paper Table of contents Abstract Introduction Study Area Log data spanning the Wolfberry interval Equivalence of P-SV and SV-P imaging Examples of P-P and SV-P profiles …

Fabric and Internal Architecture of Permian Basin Turbidites Indicated by Unsupervised Machine Learning Analysis of P-P and SV-P Images Read More »

In this paper, authors suggest a workflow that enables interpreters to apply principal component analysis (PCA) and self- organizing maps (SOM) on the most appropriate mathematically independent seismic attributes to get one classification volume. The aim is to obtain one clustered volume that best shows all small faults affecting the area.

Sharareh Manouchehri, Nam Pham, Terje A. Hellem and Rocky Roden predict lithofacies and reservoir properties using multi-attribute seismic analysis based on an unsupervised machine learning process called Self-Organizing Maps (SOMs).

A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal Component Analysis, unsupervised ML through SOM multi-attribute seismic sample training and then survey classification in the zone of interest and, finally, geobodies created from classified samples of selected winning neurons, Visualization of these results are outlined in this paper.

Published in the special Machine Learning edition of First Break, this paper lays out results from multi-attribute analysis using Paradise, the AI workbench.

In a paper presented at URTeC 2019, Geophysical Insights uses Paradise machine learning software to improve resolution the reservoir intervals of the Niobrara and Codell formations using 3D seismic data of the Denver-Julesberg basin.

Carrie Laudon, Senior Geoscience Consultant with Geophsyical Insights, explores new Machine Learning applications in E&P for geoscientists in the Permian Basin.

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