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.
Tao Zhao unveils a fault detection workflow using deep learning and image processing technologies at the 2018 Annual SEG Meeting in Anaheim.
Using a new supervised learning technique, convolutional neural networks (CNN), interpreters are approaching seismic facies classification in a revolutionary way as explained by Tao Zhao at SEG Anaheim (2018).
Geoscientists Deborah Sacrey and Rocky Roden solve exploration problems using Paradise, machine learning software for seismic interpretation in the June 2018 issue of First Break.
Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics.
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.
This paper sets out a unified mathematical framework for the process from seismic samples to geobodies.
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.