Presentations

AASPI Overview - Heather Bedle

Seismic sequence attribute - Bo Zhang

Structure-oriented filtering to retain low frequency information needed for impedance inversion - Kurt Marfurt

AASPI enhancements for Friday -David Lubo-Robles

Spectral Balancing of seismic data and its applications - Satinder Chopra

Seismic impedance inversion: physics drive Vs. Data driven -  Yitao Pu

Post migration seismic data conditioning - Pamela Blanco

Curvature and aberrancy attribute applications in complex fault settings -Shuvajit Bhattacharya

Application of AASPI attributes on 4D data for HC depletion - Evan Jowers

Employing AASPI attributes to understand channel evolution on HR3D seismic in GoM - Jacob Maag

AASPI SoF on pre-stack data and the AASPI calculator to show the before filter and after - Alex Vera

Detection of sub seismic faults using attributes: An application for carbon capture and storage - David Lubo-Robles

Seismic fault surface extraction - Bo Zhang

Time-time transform applications - Marcilio Matos

New AASPI well log capabilities & 3D Visualization -David Lubo Robles

SHAP demo & explanation - David Lubo Robles

An integrated workflow of improving the accuracy of first arrivals picking via deep learning - Yitao Pu

SOM analysis of reservoir rock from amplitude based attributes - Alex Vera

Carbonate sweetspot reservoir seismic identification using unsupervised clustering - Marcus Maas

Adding more flexibility to fault "labeling" in 3D CNN Fault prediction workflow - Thang Ha

Quantitative assessment of unsupervised Machine learning methods - Karelia La Marca

Midland Sands channel systems: Seismic attributes learnings from legacy vs. HTD* seismic surveys - Laura Ortiz

Employing CMP Spectral attributes and SHAP to assess fluid type - Mario Ballinas

Statistical attributes and application to gas hydrates - Emily Jackson

AASPI & GPR applications - Bobby Buist

Seismic phase components from time-frequency analysis - Marcilio Matos

Vector plot functionality - Thang Ha

Overview of new enhancements and capabilities - Davi Lubo Robles

Multi-seismic attributes and petrophysical studies for a low-temperature geothermal field in the Netherlands - Shujavit Bhattacharya & Sumit Verma

A comprehensive comparison of generating seismic fault attribute using deep learning - Bo Zhang

Shale resources fracability prediction - Bo Zhang

Unsupervised ML applied to seismic characterization and deepwater elements characterization in Ceará Basin, Brazil - Karen Leopoldino

Study on the parameterization response of the probabilistic neural networks for seismic facies classification - Diana Salazar

Generative adversarial network for facies and reservoir classification - Alex Vera

Recent Gas Hydrate Studies at OU: An Overview and Proposed Future Work - Emily Jackson

Using AASPI’s CNN Image Classifier with microCT scanned microfossils - Bobby Buist

Semi-supervised well log correlation - Saurabh Sinha

Convolutional Neural Networks and Transfer Learning Applications in Geosciences - Rafael Pires de Lima

Common pitfalls and limitations in seismic attribute analysis - Kurt Marfurt

A new workflow for multi-well lithofacies interpretation integrating joint petrophysical inversion, unsupervised and supervised machine learning - Sumit Verma, Shuvajit Bhattacharya, Nur Uddin Md Khaled Chowdhury, and Miao Tian

Unsupervised machine learning for interpreting shelf-to-basin seismic geomorphology and paleoclimate - Sumit Verma and Shuvajit Bhattacharya

Machine learning for fault identification - So many methods! So many faults! - Heather Bedle, Christ Ramos, Edimar Perico, and Jose Pedro Mora

Using synthetic seismic data to quantify uncertainty in machine learning - Karelia La Marca, Heather Bedle, Kurt Marfurt, Laura Ortiz, Lisa Stright, and Rafael Pires de Lima

Sensitivity analysis of seismic attribute parameterization for interpretation of a multi-story deepwater channel system: Tres Pasos Formation, Magallanes Basin, Chile - Karelia La Marca, Heather Bedle, Kurt Marfurt, Lisa Stright, and Teresa Langenkamp

From fault picks to fault probabilities to fault surfaces - Snapping and the snake algorithm - Jose Pedro Mora, Heather Bedle, and Kurt Marfurt

Seismic attributes - A promising aid for hydrocarbont prediction in deepwater of the Ceará Basin, Brazilian Equatorial Margin
Karen M. Leopoldino Oliveira, Karelia la Marca-Molina, and Heather Bedle

Low saturation gas reservoir discrimination using Self-Organizing Maps (SOM), Deep Water Gulf of Mexico
Julian Chenin and Heather Bedle

Sequence stratigraphy and application of SOM for identification of architectural elemets - A case study of the Cenozoic deep-water strata in the Northern Carnarvon Basin, Australia
Laura Oritz Sanguino, Javier Tellez, and Heather Bedle

Regional characterization of the Woodford Shale organic matter content and thermal maturity across the state of Oklahoma, USA
Emilio Torres, Roger Slatt, Paul Philp, and D. M. Jarvie

Seismic attribute optimization for deepwater facies in self-organized mapping (SOM) analysis
Karelia La Marca Molino and Heather Bedle

Seismic interpretation of structural features in the Kokako 3D seismic area, Taranaki Basin, New Zealand
Edimar Perico and Heather Bedle

Application of seismic attributes and machine learning for imaging submarine slide blocks on the North Slope, Alaska
Shuvajit Bhattacharya, Miao Tan, Jon Rotzien, and Sumit Verma

Multispectral aberrancy
Bin Lyu, Jie Qi, Fangyu Li, and Kurt J. Marfurt

Constructing fault surface objects from fault sensitive attributes
Jose Pedro Mora, Heather Bedle, and Kurt J. Marfurt

Machine learning techniques applied to angle stacks for seismic facies classification
Clayton Silver, Heather Bedle, and Matthew Rine

Application of unsupervised machine learning techniques in sequence stratigraphy and seismic geomorphology: A case study in the Cenozoic deep-water deposits in the Northern Carnarvon Basin, Australia
Laura Oritz Sanguino, Javier Tellez, and Heather Bedle

Automatic horizon picking using a jigsaw puzzle strategy
Bo Zhang and Yihuai Lou

Automatic seismic fault surface construction using seismic discontinuity attributes
Bo Zhang and Yihuai Lou

Machine learning model interpretatability using SHapley Additive exPlanations (SHAP) values: Application to a seismic facies classification task
David Lubo-Robles, Deepak Devegowda, Vikram Jayaram, Heather Bedle, Kurt J. Marfurt, and Matthew J. Pranter

Comparing convolutional neural network and image processing seismic fault detection methods
Bin Lyu, Jie Qi, Xinming Wu, and Kurt Marfurt

Seismic attenuation measurement by sparse-pulse decomposition of seismic images
Yichuan Wang, Kurt Marfurt, Igor Morozov, and Heather Bedle

Seismic attribute optimization for deepwater facies in self-organized maps (SOM) analysis
Karelia La Marca-Molina and Heather Bedle

Machine learning techniques to assist natural fracture systems using coda wave interferometry
Alexandro Vera-Arroyo and Heather Bedle

Subseismic reef characterization using machine learning and multiattribute analysis
Carl Buist, Heather Bedle, and Matthew Rine

Woodford Shale enclosed minibasin fill on the Hunton paloe shelf - A depositional model for unconventional resource plays
Emilio Torres

Woodford Shale enclosed minibasin fill on the Hunton paloe shelf - A depositional model for unconventional resource plays
Emilio Torres