Abstract: Forecasting Coalbed Gas Resources Amount by Artificial Neural Network, by Yang, Yuping; #90163 (2013)

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ABSTRACT: Facies Modeling of Tight Gas Reservoir Using Neural Network: Case Study of NIKANASSIN Formation in Canadian Foothills; #90108 (2010)

Jack H. Deng

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...ABSTRACT: Facies Modeling of Tight Gas Reservoir Using Neural Network: Case Study of NIKANASSIN Formation in Canadian Foothills; #90108 (2010) Jack H...

CNN for image super-resolution of airborne magnetic data in Ontario, Canada

Rafael Pires de Lima

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks applications in two main aspects. First, it samples training data from the full maps, accommodating missing values in the process...

GAN-based priors for Bayesian inference of subsurface geology at large scale

Kevin B. Daly, Tuan A. Tran, Brent D. Wheelock, Grant J. Seastream

International Meeting for Applied Geoscience and Energy (IMAGE)

... is typically distributed as a standard multivariate normal. Then, that vector is mapped to pixel space via a trained neural network, generating the output image...

Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction

Tao Zhao, Haibin Di, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... neural networks: A survey: IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 4037–4058, doi: https://doi.org...

Joint inversion of magnetotelluric and seismic travel time data with intelligent interpretation of geophysical models

Hongyu Zhou, Rui Guo, Maokun Li, Fan Yang, Shengheng Xu, Zuzhi Hu, Deqiang Tao

International Meeting for Applied Geoscience and Energy (IMAGE)

... with deep learning. A deep neural network for joint interpretation, namely IntNet, is seamlessly inserted into the inversion process to incorporate...

Earth Model Building in Real-Time with an Automated Machine Learning Framework - A Midland Basin Example

Altay Sansal, Muhlis Unaldi, Edward Tian, Gareth Taylor

Unconventional Resources Technology Conference (URTEC)

... and geophysics, a class of feedforward artificial neural networks (ANN), more precisely termed a Multilayer Perceptron (MLP), is used in this study to predict...

Intelligent Prediction of Shale Oil Fracturing Curves Based on A Sequence-to-Sequence Model

Leyi Zheng, Tianbo Liang, Yunjin Wang, Fujian Zhou, Junlin Wu, Bin Wang, Jiaming Zhang, Maoqin Yang, Gong Chen, Xingyuan Liang

Unconventional Resources Technology Conference (URTEC)

..., and neural networks show an initial success in time-series prediction tasks (Al Kadem et al., 2024; Elahifar et al., 2024). Ben et al. (2024) utilized...

Abstract: Neural Network Processing of High Sensitivity Aeromagnetic Data and Integration with Subsurface Geology, Paleozoic Play, Nebraska Panhandle, USA, by J. C. Webb and W. R. Pearson; #90959 (1995).

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Carbonate Reservoir Characterization Using Sequential Hybrid Seismic Rock Physics and Artificial Neural-Network: A Case Study of Tiaka Field

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A New Method to Improve the Prediction Accuracy on TOC of Source Rock by BP Neural Network Model

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Petrophysical and Geochemical Interpretations of Well Logs from the Pre-Carboniferous Succession in Barnicarndy 1, Canning Basin, Western Australia

Liuqi Wang, Dianne S. Edwards, Adam Bailey, Lidena K. Carr, Chris J. Boreham, Emmanuelle Grosjean, Leon Normore, Jade Anderson, Amber J. M. Jarrett, Susannah MacFarlane, Chris Southby, Chris Carson, Kamal Khider, Paul Henson, Peter Haines, Mike Walker

Australian Petroleum Production & Exploration Association (APPEA) Journal

..., S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy 67(1–2), 17–35. doi:10.1016/S0306-2619(00)00005-2 Kennard, J. M...

Deep Learning Models for Methane Emissions Identification and Quantification

Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan

Unconventional Resources Technology Conference (URTEC)

..., ImageNet Classification with Deep Convolutional Neural Networks: Advances in Neural Information Processing Systems, 25. https://doi.org...

Evaluation and Application of Horizontal Granite Wash Well Performance Drivers into Completion and Fracture Stimulation Design, #42149 (2017).

Barry Dean, Karthik Srinivasan, Matt Belobraydic, Wing Lam

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... Artificial Neural Systems: Oklahoma Geological Survey Circular 104, p. 65-70. Shelley, R.F., and W.V. Grieser, 1999, Artificial Neural Networks Enhanced...

Machine-learning prediction of slope channel facies using outcrop analog data: Tres Pasos Formation, Magallanes Basin, Chile

Patrick Ronnau, Lisa Stright, Stephen M. Hubbard, Brian W. Romans

International Meeting for Applied Geoscience and Energy (IMAGE)

... Geologists REFERENCES Bhatt, A., 2002, Reservoir properties from well logs using neural networks: Ph.D. dissertation, Norwegian University of Science...

A novel approach to hydrocarbon reserves estimation through the integration of AI-based solutions: 3D gamma-ray prediction and 3D seismic clustering

Konstantin Matrosov, Orkhan Mammadov, Tarek Eliva, Ruslan Malikov, Izat Shahsenov

International Meeting for Applied Geoscience and Energy (IMAGE)

... can be particularly evident in fields with complex, well-established channel networks, where the subjective nature of interpretation can heavily...

ABSTRACT: Reservoir Characterization from Seismic and Well Control with Intelligent Computing Software; #90013 (2003)

MARK SIPPEL

Search and Discovery.com

.... The software uses clustering, artificial neural networks and classical regression methods to combine seismic, geologic and engineering data for predictions...

AAPG/EAGE International Research Conference, October 1-5, 2000 - El Paso, Texas; Abstracts #90049 (2005)

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Using Machine Learning Techniques for Mapping Dolomitic Facies in a Triple Porosity Calcareous Reservoir. Campeche Sound, Gulf of Mexico; #42472 (2019)

Antonio Cervantes-Velazquez, Jerson J. Tellez, Karelia La Marca, Kurt Marfurt

Search and Discovery.com

... in pairs in well probes Final selected attributes Weighted linear combination Artificial neural networks PSTM PSDM ANN Cross-section along...

GAN based data enhancement for first arrival picking on both onshore and offshore seismic data

Ding Jicai, Wei Yanwen, Wang Yichuan, Sun Wenbo, Wang Jianhua

International Meeting for Applied Geoscience and Energy (IMAGE)

... and Welling, 2013), Generative Adversarial Networks (GAN) (Goodfellow et al., 2014), Normalizing Flows (NF) (Dinh et al., 2014) and Diffusion Model (Ho et...

Integrated networks for viscoelastic FWI: Mapping from Q to relaxation variables and quantifying modelling error

Tianze Zhang, Kristopher A. Innanen, Daniel Trad

International Meeting for Applied Geoscience and Energy (IMAGE)

...Integrated networks for viscoelastic FWI: Mapping from Q to relaxation variables and quantifying modelling error Tianze Zhang, Kristopher A. Innanen...

Abstract: Applying New Technologies to Old Areas: Relative Geologic Time, Wheeler Diagrams and Near Field Exploration in Faulted Plays

Steve Tobias

Houston Geological Society Bulletin

... South Bay Resources in 2003. It was extremely successful in using neural networks in the exploration of onshore Texas and Alberta, until it wasn't...

Abstract: Applying New Technologies to Old Areas: Relative Geologic Time, Wheeler Diagrams and Near Field Exploration in Faulted Plays

Steve Tobias

Houston Geological Society Bulletin

... Bay Resources in 2003. It was extremely successful in using neural networks in the exploration of onshore Texas and Alberta, until it wasn't. Steve...

A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature Oil Field in Texas

Felipe Ospina, Pallav Sarma, Carlos Calad

Unconventional Resources Technology Conference (URTEC)

...A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature...

Innovating Oil and Gas Forecasting: Developing a Trailblazing Generative AI Model

Akash Sharma, Scott Pellegrino, Sai Sidharth Manikandan

Unconventional Resources Technology Conference (URTEC)

... on deep learning models, particularly neural networks inspired by the human brain's structure. These networks, composed of layers of interconnected nodes...