Seismic resolution enhancement with self-supervised learning

Shijun Cheng, Tariq Alkhalifah, Haoran Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of seismic processing (Cheng et al., 2023b), some researchers have introduced techniques for enhancing seismic resolution based on neural networks...

Machine-learning based arrival-picking in continuous DAS recordings „ Application to the Utah FORGE EGS project

Nepomuk Boitz, William Tegtow, Serge Shapiro

International Meeting for Applied Geoscience and Energy (IMAGE)

... Boitz, N., and S. Shapiro, 2024, Detection of microseismic events in continuous DAS data using convolutional neural networks: The Leading Edge, 43, 16...

Leveraging source-over-cable marine seismic field data for near offset reconstruction with deep learning

Owen Rohwer Huff, Jan Erik Lie, Andreas Kjelsrud Evensen, Aina Juell Bugge

International Meeting for Applied Geoscience and Energy (IMAGE)

... data collected with source-over-cable acquisition geometry as training data. First, a convolutional neural network (CNN) is trained to reconstruct...

Reservoir characterization, modeling, and evaluation of Upper Jurassic Smackover microbial carbonate and associated facies in Little Cedar Creek field, southwest Alabama, eastern Gulf coastal plain of the United States

Sharbel Al Haddad, Ernest A. Mancini

AAPG Bulletin

..., A., 2002, Reservoir properties from well logs using neural networks: Ph.D. dissertation, Norwegian University of Science and Technology, Trondheim, Norway...

Abstract: Successful Application of Neural Network Technique for Rock Typing Marrat Carbonate Reservoirs of West Kuwait, by Naveen Kumar Verma, Fahed Al-Medhadi, Christian Perrin, Rasha Al-Moraikhi, Eman Al-Mayyas, and D. Natarajan; #90077 (2008)

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Statistical Variable Salt Velocity Calculation by Neural Network Classification in the Central Gulf of Mexico

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Abstract: Implementation of Seismic Salt Interpretation by a Deep Neural Network with Neural Style Transfer; #91204 (2023)

Fan Jiang, Konstantin Osypov, Julianna Toms

Search and Discovery.com

...Abstract: Implementation of Seismic Salt Interpretation by a Deep Neural Network with Neural Style Transfer; #91204 (2023) Fan Jiang, Konstantin...

SaltCrawler: AI solution for accelerating velocity model building

Engin Alkan, Yihua Cai, Pandu Devarakota, Apurva Gala, John Kimbro, Dean Knott, Gislain Madiba, Jeff Moore

International Meeting for Applied Geoscience and Energy (IMAGE)

... building to accurately describe salt edges, flanks, and overhangs. The deep convolutional neural networks (CNN) were trained under a multi-task learning...

Prediction of porosity in CO2 sequestration tight reservoirs based on multi-source and multi-scale data fusion

Ping Lu, Peixue Jiang, Ruina Xu, Huaqing Xue, Haojie Li, Lei Yu, Jiashu Han

International Meeting for Applied Geoscience and Energy (IMAGE)

... the commonly used machine learning algorithms such as neural networks mainly use logging or seismic attributes as feature variables and do not introduce...

Unsupervised Machine Learning Applications for Seismic Facies Classification

Satinder Chopra, Kurt J. Marfurt

Unconventional Resources Technology Conference (URTEC)

..., the attribute vector at a given voxel is then assigned to the cluster to which it is nearest. Although the term neural networks and clustering are often used...

Automated Data and ML Pipelines to Accelerate Subsurface Digitalization

Raj Kannan, Vikas Jain

Unconventional Resources Technology Conference (URTEC)

... effort. Supervised ML models based on algorithms such as convolutional neural networks (CNNs) can work best to solve the picking problem. b. Leveraging...

Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs

Y. Kocoglu, M. E. Wigwe, G. Sheldon, M. C. Watson

Unconventional Resources Technology Conference (URTEC)

..., one of the well-known ANN methods, called Feed Forward Neural Networks (FFNN), was applied to predict the 25-year gas EUR potential of undrilled...

Using Strike Resolved by Neural-Optic Processing to Determine Dip in Complex Structures Penetrated by Deviated Wells, by M. B. Enderlin and D. Lenzer; #90986 (1994).

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Abstract: Prediction of Mesaverde Estimated Ultimate Recovery Using Structural Curvature and Neural Network Analysis, San Juan Basin, New Mexico, by P. M. Basinski, A. M. Zellou, and A. Ouenes; #90946 (1997).

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Abstract: Neural Net Generated Classification and Near Reflectors Geometry Maps In Seismic Stratigraphic Interpretation, by S. K. Addy; #90928 (1999).

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Abstract: Neural Permeability Prediction of Heterogeneous Gas Sand Reservoirs, by Gharib M. Hamada and Moustafa Elshafei; #90105 (2010)

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ABSTRACT: Delineation of Ac Reservoir Sand from Multi-Attribute Analysis of Well and Seismic Data … A Case Study from Awali Field Bahrain; #90051 (2006)

Ravi Kant Pathak, Yahya Mohamed Al-Ansari

Search and Discovery.com

... implemented through neural network solution. The paper illustrates the adopted workflow and outcome of the study. The essential components...

Abstract: Understanding the High Resolution Facies Variation in Cretaceous Carbonates with Image Logs and Neural Network: A Multi-well Study from Offshore Abu Dhabi; #90254 (2016)

Sato Fumitoshi, Mizuno Tatsuya, Chandramani Shrivastava, Sajith Girinathan, and Jaja Uruzula

Search and Discovery.com

...Abstract: Understanding the High Resolution Facies Variation in Cretaceous Carbonates with Image Logs and Neural Network: A Multi-well Study from...

Abstract: Fast-Track and Robust Reservoir Modeling Using Probabilistic Neural Network; #90319 (2018)

Islam A. Mohamed, Basem K. Abd El-Fattah

Search and Discovery.com

...Abstract: Fast-Track and Robust Reservoir Modeling Using Probabilistic Neural Network; #90319 (2018) Islam A. Mohamed, Basem K. Abd El-Fattah AAPG...

Abstract: Enhancement of 3-D Fault Interpretation from Seismic Data Using a Post-Stack Seismic Data Conditioning and Artificial Neural Network Approach in Cai36 3-D Prospect of the Junggar Basin, China;

Lijie Cui, Kongyou Wu, Jie Ji

Search and Discovery.com

...Abstract: Enhancement of 3-D Fault Interpretation from Seismic Data Using a Post-Stack Seismic Data Conditioning and Artificial Neural Network...

Seismic data on the workbench

Brit Sauar, Anders Kihlberg, Henk Kombrink

GEO ExPro Magazine

... dominates much of the subsurface geoscience narrative, challenges remain when applying deep learning to largescale seismic interpretation. “Neural...

Seismic reservoir characterization of the Strawn Group, northern part of the Eastern Shelf, King County, North-Central Texas: Case study

Osareni C. Ogiesoba

International Meeting for Applied Geoscience and Energy (IMAGE)

... ratio Next, I predicted density volume by using multiattribute linear regression and probabilistic neural networks (PNN) methods. The methods entail...

Deep Learning for Quantitative Hydraulic Fracture Profiling from Fiber Optic Measurements

Weichang Li, Han Lu, Yuchen Jin, Frode Hveding

Unconventional Resources Technology Conference (URTEC)

... using deep neural networks Figure 4 shows the training and testing performance for one of the stages using Resnet model. LSTM model provided smoother...

Automated machine learning first-break picking in the Sichuan Basin „ A case study

Jianfa Wu, Xuewen Shi, Qiyong Gou, Ersi Xu, Dongjun Zhang, Dingxue Wang, Phil Bilsby, Qing Zhou, Rong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... neural networks (CNNs) to act as three independent first-break picking ‘experts’ (Figure 1c). The first CNN model has a U-net architecture (Ronneberger...

Identifying geologic facies through seismic dataset-to-dataset transfer learning using convolutional neural networks

Joseph Stitt, Adam Shugar, Rachael Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

...Identifying geologic facies through seismic dataset-to-dataset transfer learning using convolutional neural networks Joseph Stitt, Adam Shugar...