ABSTRACT Characterization and Modeling of a CO2 Huff n Puff to Predict and Verify EOR Production and CO2 Storage, #90104 (2010)

Knudsen Damion J.; Gorecki Charles D.; Bremer Jordan M.; Holubnyak Yevhen I.; Mibeck Blaise A.; Schmidt Darren D.; Smith Steven A.; Sorensen James A.; Steadman Edward; Harju John A.

Search and Discovery.com

... analysis. Neural networks were used to produce matrix permeability, fracture density, and missing zones or logs in the study area.  Petrophysical results...

ABSTRACT Regional Analysis of the Permian Upper Minnelusa Formation, Powder River Basin, Wyoming: Application to Exploration and Development, #90104 (2010)

Tomasso Mark; Murrell Glen; Reyes Brian M.; Thyne Geoff; Forney Gerald G.; Shier Daniel D.

Search and Discovery.com

... sandstone, laminated dolomitic mudstone‐sandstone,  dolomite, and massive anhydrite. An unconstrained estimation model was carried out using neural...

ABSTRACT: The Permian Upper Minnelusa Formation, Powder River Basin, Wyoming: Regional Analysis and Application to Exploration and Development; #90106 (2010)

Mark Tomasso, Glen Murrell, Brian M. Reyes, Geoffrey Thyne, Gerald G. Forney, Daniel E. Shier

Search and Discovery.com

..., dolomite, and massive anhydrite. An unconstrained estimation model was carried out using neural networks to correlate the gamma and sonic logs...

AAPG Geoscience Technology Workshop; - Abstracts, #90227 (2015).

Search and Discovery.com

Abstract: Total Organic Carbon Prediction from Well Logs Using the Support Vector Regression Technique; #90254 (2016)

Mohamad Shahab, Guodong Jin, and Gaurav Agrawal

Search and Discovery.com

... intelligence technique, which integrates the vast amount of logging data to predict unknown properties. Unlike the extensively used artificial neural...

Abstract: Modeling Crude Oil Mobility of Unconventional Tight Carbonate Reservoir; #91204 (2023)

Huda Alnasser, Mohammad Al-Senafy, Waleed Al-Bazzaz, Salem Al-Sabea, Khaled Ziyab, Bader Al-Mal, Taher Gezeeri, Dalal Alrayahi

Search and Discovery.com

... approach of modeling based on Neural Networks is used to characterize the permeability (K), viscosity (μ) and Mobility (K/μ) of this tight carbonate...

Some Machine Learning Applications in Seismic Interpretation; #42270 (2018)

Satinder Chopra, David Lubo-Robles, Kurt J. Marfurt

Search and Discovery.com

... ● Multilinear feedforward neural networks ● Probabilistic neural networks ● Support vector machines Unsupervised learning is slightly more difficult...

Multiscale fault and fracture characterization methods

QiQi Ma, Taizhong Duan

International Meeting for Applied Geoscience and Energy (IMAGE)

..., H. B., Z. Wang, and G. AlRegib, 2018, Seismic fault detection from post-stack amplitude by convolutional neural networks: 80th Conference...

A geophysical prior knowledge guided semisupervised deep learning framework for AVA inversion

Lei Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

... intelligent inversion results reliable. The framework contains three branch networks of reservoir parameters. Each branch network contains...

High-resolution seismic data processing method based on deep convolutional dictionary learning

Xiayu Gao, Qingyu Feng, Yaojun Wang, Bangli Zou, Yang Luo

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the neural network; L ( ) represents the loss function. Unlike traditional dictionary learning methods, we employ deep neural networks, Net-X and Net...

Fast, Fully Probabilistic, Nonlinear Inversion of Seismic Attributes for Petrophysical Parameters; #120047 (2012)

Andrew Curtis, Mohammad Shahraeeni, and Gabriel Chao

Search and Discovery.com

... based on neural networks to predict 3D petrophysical properties from inverted prestack seismic data. The objective of petrophysical inversion...

Fault MLReal: A fault delineation study for the Decatur CO2 field data using neural network predicted passive seismic locations

Hanchen Wang, Yinpeng Chen, Tariq Alkhalifah, Youzuo Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... and performance of Convolutional Neural Networks (CNN). Considering we have labeled training data, referred to in domain adaptation circles...

Unsupervised frequency…space domain deep learning framework for reconstructing 5D seismic data

Gui Chen, Yang Liu, Haoran Zhang, Mi Zhang, Yuhang Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

... to improve seismic imaging. We introduce a deep complex-valued neural network for constructing an unsupervised frequency–space domain deep learning...

Multi-Modal Neural Network for Porosity and Permeability Estimation in Tight Gas Reservoirs: A Case Study in the Ordos Basin, China

Shengjuan Cai, Yitian Xiao, Han Wang, Feifei Gou, Hanqing Wang, Yujie Zhou, Tianrui Ye

Unconventional Resources Technology Conference (URTEC)

..., P., Clark, S. R., and Armstrong, R. T. 2021. Automated lithology classification from drill core images using convolutional neural networks. Jour...

Machine Learning using Multiple Seismic Attributes could be the Paradigm Shift in the Interpretation Process

Deborah K. Sacrey

GCAGS Transactions

... the conventional amplitude wavelet interpretation. This paper shows examples of problems in the everyday interpretation of data which can be solved by the neural...

Hydrocarbon Saturation Prediction from Full-Stack Seismic Data Using Probabilistic Neural Network, #41881 (2016).

Islam A. Mohamed

Search and Discovery.com

...Hydrocarbon Saturation Prediction from Full-Stack Seismic Data Using Probabilistic Neural Network, #41881 (2016). Islam A. Mohamed Hydrocarbon...

Petrophysical Characterisation of the Neoproterozoic and Cambrian Successions in the Officer Basin

Liuqi Wang, Adam H. E. Bailey, Lidena K. Carr, Dianne S. Edwards, Kamal Khider, Jade Anderson, Christopher J. Boreham, Chris Southby, David N. Dewhurst, Lionel Esteban, Stuart Munday, Paul A. Henson

Australian Petroleum Production & Exploration Association (APPEA) Journal

... will be discussed in the next section. Interpretation with artificial neural networks (ANNs) Permeability is a key parameter for reservoir characterisa­ tion...

Machine learning and explainable AI for predicting missing well log data with uncertainty analysis: A case study in the Norwegian North Sea

Sushil Acharya, Karl Fabian

International Meeting for Applied Geoscience and Energy (IMAGE)

..., particularly neural networks, has gained significant attention in predicting missing well logs. Pham et al. (Pham, Wu et al. 2020) introduced a data...

Geochemical determination of heavy oil viscosity using multivariate statistical algorithms, #90110 (2010)

Jennifer Adams, Barry Bennett, Lloyd Snowdon, Steve Larter

Search and Discovery.com

... methods, e.g., partial least squares (PLS; Rosipal & Kramer, 2006) and neural networks (Qin & McAvoy, 1992) have been applied to complex oil, coal or other...

DL-fused elastic FWI: Application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... residual network, which resolves some common issues in deep neural networks (i.e., gradient vanishing). We increased the number of filters in each...

Towards Universal Production Forecasting via Adversarial Transfer Learning and Transformer with Application in the Shengli Oilfield, China

Ji Chang, Jin Meng, Dongwei Zhang, Tianrui Ye, Han Wang, Yitian Xiao

Unconventional Resources Technology Conference (URTEC)

... learning-based production forecasting aims to learn latent decline patterns from historical production data using deep neural networks to forecast...

A deep learning-based inverse Hessian for full-waveform inversion

Mustafa Alfarhan, Matteo Ravasi, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... of images, the use of neural networks to approximate it is attractive due to their success in superresolution applications. Despite the aforementioned...

Seismic absolute acoustic impedance inversion using domain adversarial based transfer learning

Anjali Dixit, Animesh Mandal

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Laviolette, M. March, and V. Lempitsky, 2016, Domain adversarial training of neural networks: Journal of Machine Learning Research, 17, no. 59, 1–35. Ghosh...

Seismic Quantitative Analysis for Physical-Based Deep Learning: The Teapot Dome and Niobrara Shale Examples

Nicolas Martin, Maria Donati

Unconventional Resources Technology Conference (URTEC)

... type (i.e., brine, oil and gas) and prediction of fracture density (proxy) distributions by using LSTM deep recurrent and neural networks, respectively...

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann

International Meeting for Applied Geoscience and Energy (IMAGE)

.../10.1190/geo2020-0152.1. Yosinski, J., J. Clune, Y. Bengio, and H. Lipson, 2014, How transferable are features in deep neural networks? Proceedings of the 27th...