Fracture Modeling in Petrel

Daniel Rivas

Search and Discovery.com

... with fracture zones, and Neural Networks, which is able to create 3D properties based on well data or well+seismic data. Some other workflows are based...

Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement

Kirill Gadylshin, Ilya Silvestrov, Andrey Bakulin

International Meeting for Applied Geoscience and Energy (IMAGE)

... is similar to object detection problems in computer vision. Deep neural networks for image classification are used in seismic attributes analysis (Das et...

New Technology to Identify and Characterize Natural Fractures

W. W. Weiss, Abdel Zellou

Four Corners Geological Society

..., plus thickness and lithology, with fracture frequency as defined by production. Neural networks are well suited to handling multiple parameter...

Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia

Mohammed Farfour, Douglas Foster

International Meeting for Applied Geoscience and Energy (IMAGE)

...-saturated reservoirs from Poseidon field, Offshore Australia. Feedforward Artificial Neural Networks (ANN) are implemented to combine seismic attributes...

HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES

Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov

Journal of Sedimentary Research (SEPM)

...., Swietojanski, P., Clark, S.R., and Armstrong, R.T., 2020, Automated lithology classification from drill core images using convolutional neural networks...

Source location using physics-informed neural networks with hard constraints

Xinquan Huang, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

...Source location using physics-informed neural networks with hard constraints Xinquan Huang, Tariq Alkhalifah Source location using physics-informed...

Automated metallic pipeline detection using magnetic data and convolutional neural networks

Brett Bernstein, Yaoguo Li, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

...Automated metallic pipeline detection using magnetic data and convolutional neural networks Brett Bernstein, Yaoguo Li, Richard Hammack Automated...

Solution for Anomaly Detection in Oil & Gas Well Drilling Sensors Based on Recurrent Neural Networks: A Big Data Approach

María Yamile Orellano, Marilyn Vargas, Julián Benavidez

Unconventional Resources Technology Conference (URTEC)

...Solution for Anomaly Detection in Oil & Gas Well Drilling Sensors Based on Recurrent Neural Networks: A Big Data Approach María Yamile Orellano...

Improving Resolution and Clarity with Neural Networks; #41911 (2016)

Christopher P. Ross

Search and Discovery.com

...Improving Resolution and Clarity with Neural Networks; #41911 (2016) Christopher P. Ross GC Improving Resolution and Clarity with Neural Networks...

Semi-supervised ground-roll wave segmentation with noise label via neural network evolution

Yinshuo Li, Wenkai Lu

International Meeting for Applied Geoscience and Energy (IMAGE)

... achieve better performance than the label. Meanwhile, the co-evolution of neural networks and data is utilized to annotate more data and achieve better...

Definition of geohazards in exploration 3-D seismic data using attributes and neural-network analysis

Roar Heggland

AAPG Bulletin

.... M., 1999a, Seismic reservoir characterisation using artificial neural networks: 19th Mintrop-Seminar, May 1618, 1999, Mnster, Germany.de Groot, P. F...

An Integrated Deep Learning Solution for Petrophysics, Pore Pressure, and Geomechanics Property Prediction

Ehsan Zabihi Naeini, Sam Green, Marianne Rauch-Davies

Unconventional Resources Technology Conference (URTEC)

... and interpreted data to devise solutions which simultaneously integrate wide ranging well bore and wireline logs. We implement three neural networks, all...

Application of Bayesian Optimized Deep Bi-LSTM Neural Networks for Production Forecasting of Gas Wells in Unconventional Shale Gas Reservoirs

Y. Kocoglu, S. Gorell, P. McElroy

Unconventional Resources Technology Conference (URTEC)

...Application of Bayesian Optimized Deep Bi-LSTM Neural Networks for Production Forecasting of Gas Wells in Unconventional Shale Gas Reservoirs Y...

Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Unconventional Resources Technology Conference (URTEC)

...; Zobeiry and Humfeld, 2021) use data-driven methods (e.g., neural networks) to explicitly perform physical computations to solve or rather, approximate...

Multilayer perceptron and Bayesian neural network based implicit elastic full-waveform inversion

Tianze Zhang, Jian Sun, Daniel O. Trad, Kristopher A. Innanen

International Meeting for Applied Geoscience and Energy (IMAGE)

... networks: a neural network that generates velocity models, and a recurrent neural network to perform the modeling. The approach is distinct from conventional...

Geological Interpretation Using Pattern Recognition from Self-Organizing Maps and Principal Component Analysis

Search and Discovery.com

Predicting Facies Distribution Within a Fluvial System in the Subsurface: Triassic of the Central North Sea

Search and Discovery.com

Reservoir Modeling With Deep Learning

Search and Discovery.com

ABSTRACT: An iterative method for identifying seismic objects by their texture, orientation and size; #90007 (2002)

Paul Meldahl and Roar Heggland, Bert Bril and Paul de-Groot

Search and Discovery.com

... of semi-automated detection of seismic objects by directive attributes and neural networks: Part II; Interpretation. Annual Meeting. Society...

ABSTRACT: Use of Seismic Character for Intelligent Facies Analysis and Reservoir Property Estimation; #90007 (2002)

Fred Aminzadeh, Paul. F. M. de Groot

Search and Discovery.com

... in training the supervised neural networks. The pseudo-well simulator generates stratigraphic columns with the corresponding well logs using a constrained...

ABSTRACT: Statistically-Based Lithofacies Predictions for 3-D Reservoir Modeling: An Example from the Panoma (Council Grove) Field, Hugoton Embayment, Southwest Kansas; #90013 (2003)

Martin K. Dubois, Alan P. Byrnes, Geoffrey C. Bohling, Shane C. Seals, John H. Doveton

Search and Discovery.com

... reservoirs like the Panoma Field, but prediction tools, neural networks and the Excel add-in Kipling.xla, a nonparametric discriminant analysis tool, provide...

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Search and Discovery.com

Simulating seismic data using generative adversarial networks

Bradley C. Wallet, Eyad Aljishi, Hussain Alfayez

International Meeting for Applied Geoscience and Energy (IMAGE)

...Simulating seismic data using generative adversarial networks Bradley C. Wallet, Eyad Aljishi, Hussain Alfayez Second International Meeting...

A self-attention enhanced encoder-decoder network for seismic data denoising

Stefan Knispel, Jan Walda, Ruediger Zehn, Alexander Bauer, Dirk Gajewski

International Meeting for Applied Geoscience and Energy (IMAGE)

... deep neural networks, can be applied to a large variety of recognition tasks, e.g. the well-known U-Net for detecting cancer cells in biomedical...

Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks

Sixiu Liu, Claire Birnie, Tariq Alkhalifah, Andrey Bakulin

International Meeting for Applied Geoscience and Energy (IMAGE)

... growth of deep learning procedures, Neural Networks (NNs) have been extensively used to suppress noise in seismic data. Such denoising methods often...