scholarly journals MODELING OF GEOBODIES: AI FOR SEISMIC FAULT DETECTION AND ALL-QUADRILATERAL MESH GENERATION

2018 ◽  
Author(s):  
AXELLE DANY JULIETTE POCHET
Keyword(s):  
Fault Detection ◽  
Mesh Generation ◽  
Quadrilateral Mesh ◽  
Seismic Fault

scholarly journals Quadrilateral mesh generation III : Optimizing singularity configuration based on Abel–Jacobi theory

2021 ◽  
Vol 387 ◽  
pp. 114146
Author(s):  
Xiaopeng Zheng ◽  
Yiming Zhu ◽  
Wei Chen ◽  
Na Lei ◽  
Zhongxuan Luo ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Quadrilateral Mesh

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

2019 ◽  
Vol 16 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Axelle Pochet ◽  
Pedro H. B. Diniz ◽  
Helio Lopes ◽  
Marcelo Gattass
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Convolutional Neural Networks ◽  
Seismic Fault

Quadrilateral mesh generation I : Metric based method

2019 ◽  
Vol 356 ◽  
pp. 652-668 ◽  
Author(s):  
Wei Chen ◽  
Xiaopeng Zheng ◽  
Jingyao Ke ◽  
Na Lei ◽  
Zhongxuan Luo ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Quadrilateral Mesh

Seismic fault detection with iterative deep learning

2019 ◽  
Author(s):  
Ruoshui Zhou ◽  
Yufei Cai ◽  
Fucai Yu ◽  
Guangmin Hu
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Seismic Fault

NURBS based scheme for automatic quadrilateral mesh generation for FE and BEM analysis

2012 ◽  
Vol 7 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Ranadev Datta ◽  
C. Guedes Soares
Keyword(s):  
Mesh Generation ◽  
Quadrilateral Mesh
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