Abstract: Unsupervised Segmentation of Rock MicroCT Scans Using Deep Learning;

Fernando Bordignon, Giovanni Formighieri, Eduardo Burgel, Bruno Rodrigues

Search and Discovery.com

... of DNNs when working with images are the Convolutional Neural Networks (CNN), usually employed in conjunction with supervised training, which needs...

Horizon detection with CNN-based multiscale volumetric flattening

Jesse Lomask

International Meeting for Applied Geoscience and Energy (IMAGE)

... combines the power of Convolutional Neural Network (CNNs) and traditional geophysical inversion methods, flattening a seismic volume into a pseudo...

Application of SVM machine learning high-resolution fusion inversion in stratigraphic correlation

Lyu Huaxing, Zhang Weiwei, Chen Zhaoming, Zhang Zhenbo, Liu Junyi, Xu Hao

International Meeting for Applied Geoscience and Energy (IMAGE)

... method is needed. However, the accuracy of most model-based inversion methods depends on the initial model. The development of seismic highresolution...

Abstract: Automatic Fault Tracking from 3D Seismic Data Using the 2D Continuous Shearlet Transform with an Example from the Algerian Sahara; #91210 (2025)

Sid-Ali Ouadfeul

Search and Discovery.com

..., the seismic cube is cropped in time, to keep the seismic data corresponding to the geological target, only. Then, the variance attribute is applied...

AAPG Annual Convention and Exhibition 2020; - Abstracts, #91200 (2022).

Search and Discovery.com

What Broke? Microseismic analysis using seismic derived rock properties and structural attributes in the Eagle Ford play

Robert Meek, Bailo Suliman, Robert Hull, Hector Bello, Doug Portis

Unconventional Resources Technology Conference (URTEC)

... regression analysis technique. Rock properties and structural attributes are combined with an ellipsoid stimulation model around the well bore...

Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring

Guoxiang Liu, Abhash Kumar, Song Zhao, Chung Yan Shih, Veronika Vasylkivska, Paul Holcomb, Richard Hammack, Jeffery Ilconich, Grant Bromhal

Unconventional Resources Technology Conference (URTEC)

... of individual events, and therefore time sensitive. A multi-layered RNN including the input, output, and hidden layers comprising 1D convolutional layer...

Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data

Ruizhi Zhong, Raymond L. Johnson Jr., Zhongwei Chen

Unconventional Resources Technology Conference (URTEC)

... (Schmidhuber 2015). Supervised learning is learning a predictive model that maps certain inputs to a desired output. To build the supervised learning...

Generating Missing Unconventional Oilfield Data using a Generative Adversarial Imputation Network (GAIN)

Justin Andrews, Sheldon Gorell

Unconventional Resources Technology Conference (URTEC)

... convolutional or RELU networks, in this effort it was necessary to train the GAIN model using a hyperbolic tangent. When initially trained with RELU, the test...

Seismic-based paleoenvironmental analysis of the Paleocene carbonate shelf in Ajdabiya Trough, north-central of Libya

Abdeladim M. Asheibi

Bulletin of Canadian Energy Geoscience (CEGA)

..., constrained sparse spike inversion (Debeye and Riel, 1990). All these methods resolve the components of the convolutional model in each seismic trace...

Unlocking hidden potential in shallow water Gulf of Mexico legacy data for carbon capture and storage exploration

Rachel Collings, Igor Marino, Adriana Arroyo Acosta, Jack Kinkead, Hugo Medel, Trong Tang, Gabriela Suarez, Brett Sellers

International Meeting for Applied Geoscience and Energy (IMAGE)

... deployed a comprehensive wavelet processing workflow. To obtain a high-resolution velocity model, a seismic inversion workflow was implemented...

Application of Artificial Intelligence Tools for Fault Imaging in an Unconventional Reservoir: A Case Study from the Permian Basin

H. Garcia, L. Plant

Unconventional Resources Technology Conference (URTEC)

... analysis in a short time frame, producing a cleaner structural volume. This volume was combined with the results of the other workflows, integrating...

The Geophysical Case History of Rengasdengklok Area, North West Java

Basuki Puspoputro, Emir Lubis

Indonesian Petroleum Association

... No. 506.78.03. Robinson, E.A., 1983. Seismic velocity analysis and the convolutional model. IHRDC, Boston. Schlumberger, 1987. Well Seismic Service Processing...

GeoStreamer X Delivers Near-Field Multi-Azimuth Dataset for Accurate Lead Characterisation, South Viking Graben, Norway

Cyrille Reiser, Eric Mueller, PGS

GEO ExPro Magazine

... summarised below: • Comprehensive demultiple sequence addressing the short and long period multiples integrating 3D convolutional and wave equation...

Improved Nanoscale Image-based Reservoir Characterization using Supervised Machine Learning

Shannon L. Eichmann, Poorna Srinivasan, Kevin Kenga, Mohammed Khan, Fabian Duque, Felix Oyarzabal, James Howard, Shawn Zhang

Unconventional Resources Technology Conference (URTEC)

.... When using the SML method, manual post-processing is minimized or no longer needed. This reduces the amount of time needed for image processing per...

Label-defect tolerance ability of deep learning inversion networks and its applications

Yang Ping, Xu Hunqun, Liu Di, Tao Chunfeng, Wang Chengxiang, Yue Changqing

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion accuracy. Through model testing, we confirm that Deep Learning (DL) networks have the ability to tolerate labels’ defects, which we name...

Wave-equation based inversion for carbonate reservoir characterization in Kuwait

Rao Narhari, Ahmad Al-Sabari, Afrah Al-Ajmi, Justin Ugbo, Jeremy Harris, Gerard Soto, Panos Doulgeris

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the convolutional model synthetic compared to the seismic gather. This is because transmission is neglected in this type of modelling and in some cases...

Large Mudstone-Nucleus Sandstone Spheroids in Submarine Channel Deposits: NOTES

Daniel J. Stanley

Journal of Sedimentary Research (SEPM)

...-balls (Dzulynski, et al., 1957) and convolutional balls (Dott and Howard, 1962) may be cited as examples. The above-mentioned ball structures, however...

Combined P and S Waves Survey for Hydrocarbon Exploration

Basuki Puspoputro

Indonesian Petroleum Association

... velocity analysis and the convolutional model: IHRDC, Boston. Sheriff, R.E., 1984, Encyclopedic dictionary of exploration geophysics, 2nd edition...

High-resolution seismic detection of shallow natural gas beneath Hutchinson, Kansas

Susan E. Nissen, W. Lynn Watney, Jianghai Xia

Environmental Geosciences (DEG)

...-frequency reflections are artifacts of the overlying gas, which cannot be accurately reproduced with the simple convolutional model used to create...

AAPG ACE 2018

Search and Discovery.com

AAPG RM Section Meetings

Search and Discovery.com

Using mixture density networks for uncertainty and prediction in seismic reservoir characterization

Cornelius Rosenbaum, Ryan Warnick, Anar Yusifov, Reetam Biswas, Atish Roy

International Meeting for Applied Geoscience and Energy (IMAGE)

...–111. Khan, S., H. Rahmani, S. A. A. Shah, and M. Bennamoun, 2018, A guide to convolutional neural networks for computer vision: Synthesis Lectures...

Abstract: Different Flavors of the Marchenko-Equation-Based Internal Multiple Elimination Methods: the Trade-off between Fidelity, Computational Cost and Ease of Use; #91204 (2023)

Marcin Dukalski, Chris Reinicke

Search and Discovery.com

... that the target primaries are dressed with a convolutional filter representing the total overburden transmissions. Therefore, convolution...

NATS on WATS seismic imaging, rock property modeling and interpretation using machine-learning techniques to inform reservoir quality and deliverability in the Gulf of Mexico

Peter Lanzarone, Shenghui Li, Kang Fu, Kenny Gullette, Jeff Thompson, Gabriel Ritter

International Meeting for Applied Geoscience and Energy (IMAGE)

... as training labels input into a convolutional neural network (CNN) (e.g., Chenin, et al., 2021), where seven in-lines and twelve crosslines were labelled...