Adding Texture Attributes to the 3-D Mix*, by Paul de Groot, Farrukh Qayyum, and Nanne Hemstra; #41244 (2013).

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Predicting Hydrocarbon Production Behavior in Heterogeneous Reservoir Utilizing Deep Learning Models

Fatick Nath, Sarker Asish, Happy R. Debi, Mohammed Omar S Chowdhury, Zackary J. Zamora, Sergio Muñoz

Unconventional Resources Technology Conference (URTEC)

...; Sagheer and Mostafa, 2019; Fan et al. 2020; Kong et al. 2023) such as artificial neural networks, recurrent neural networks, long shortterm memories (LSTM...

Deep learning approach to inverting flexural wave dispersion

Hengjian Zhang, Zhifen Sun, Xianzhi Li, Xiaofang Sun, Chu Wang, Ya Jin, Xiaofei Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... uses neural networks to replace borehole acoustic field equation calculations, thereby reducing model computation complexity and retaining...

3D seismic image-to-image translation

Xiaolei Song, Muhong Zhou, Lifeng Wang, Rodney Johnston

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Uncertainty is always an inherent problem of machine learning algorithms. One solution is Bayesian neural networks (BNN; Blundell, et al., 2015). In 2020...

Alternate and Emerging Methodologies in Geochemical and Empirical Modeling

James R. Wood, Alan P. Byrnes

Special Publications of SEPM

... approaches, non-linear and/or nonparametric multivariate regression analysis, possibility analysis, and neural networks. Hybrid Process-Effect Approach...

Orogenic gold prospectivity mapping using machine learning

Mike McMillan, Jen Fohring, Eldad Haber, Justin Granek

Petroleum Exploration Society of Australia (PESA)

...), to logistic regression (Harris and Pan, 1999), to deep neural networks (Brown et al., 2000; Cracknell and Reading, 2013; Granek et al., 2016; Granek...

Coal Identification Using Neural Networks with Real-Time Coalbed Methane Drilling Data

Ruizhi Zhong, Raymond Johnson Jr, Zhongwei Chen, Nathaniel Chand

Australian Petroleum Production & Exploration Association (APPEA) Journal

...Coal Identification Using Neural Networks with Real-Time Coalbed Methane Drilling Data Ruizhi Zhong, Raymond Johnson Jr, Zhongwei Chen, Nathaniel...

Boosting self-supervised blind-spot networks via transfer learning

Claire Birnie, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

...., and F. Hansteen, 2020, Bidirectional recurrent neural networks for seismic event detection: arXiv preprint, arXiv:2012.03009. Birnie, C., K. Chambers...

Magnetotelluric inversion using supervised learning trained with random smooth geoelectric models

Lian Liu, Bo Yang, Yixian Xu, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... mechanism using artificial neural networks (ANNs) (ArayaPolo et al. 2018; Earp et al. 2020; Fabien-Ouellet & Sarkar 2020; Li et al. 2020; Zhang et al...

ABSTRACT: Deep forest cover classification of consecutive landsat imageries over Borneo

Azalea Kamellia Abdullah, Mohd Nadzri Md Reba, Nur Efarina Jali, Sikula Magupin, Diana Anthony

Geological Society of Malaysia (GSM)

... learning image classification algorithms such as Convolutional Neural Networks (CNN) attains higher accuracy mapping with low human interruption. Deep...

ABSTRACT: Insights from the Analysis of High-resolution 3-D Seismic and High-density Well Data in a Paleocene Shallow Fluvio-deltaic Reservoir with Low Net Deposition, Suriname (South America), by Luthi, Stefan M., Israel Rivera Rabelo, Aernout Schram de Jong, Hilbrand Haverkamp; #90026 (2004)

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ABSTRACT: Integrated Geomechanical Reservoir Characterization Approach to Study Migration and Accumulation of Hydrocarbons within Llanos Basin, Colombia, by Baranova, Valentina; Mustaqeem, Azer; Brouwer, Friso; Connolly, David; #90135 (2011)

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Facies prediction from seismic objects: an approach for multidisciplinary purposes

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Gee Whiz Geophysics…But What About the Log Data?, by Jeff S. Arbogast and Steven M. Goolsby, #40181 (2005).

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Application of artificial intelligence for simultaneous water and gas coning problems in hydraulically fractured tight oil reservoir

Mihir Kumar

International Meeting for Applied Geoscience and Energy (IMAGE)

... correlation for critical oil flow rate, leveraging cutting-edge artificial neural networks. By integrating 3-D reservoir simulation, a comprehensive...

An Introduction to Deep Learning: Part III

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... that deep learning is nothing other than neural networks – an approach to artificial intelligence (AI) which has been going in and out of fashion...

Revolutionizing seismic data compression: Unlocking the power of stable diffusion neural networks

Ayrat Abdullin, Umair Bin Waheed, Naveed Iqbal

International Meeting for Applied Geoscience and Energy (IMAGE)

...Revolutionizing seismic data compression: Unlocking the power of stable diffusion neural networks Ayrat Abdullin, Umair Bin Waheed, Naveed Iqbal...

Generative modeling for inverse problems

Rami Nammour

International Meeting for Applied Geoscience and Energy (IMAGE)

.... The model error would, of course, not be available in practice, but is used here for quality control after the inversion. RESULTS The neural networks...

Deep-learning application of salt geometry detection in deep water Brazil

Ruichao Ye, Anatoly Baumstein, Kirk A. Wagenvelt, Erik R. Neumann

International Meeting for Applied Geoscience and Energy (IMAGE)

..., and ergonomically challenging. Recently, salt model building has benefited from the latest developments of Deep Neural Networks (DNN) for image segmentation, which...

Estimation of Nuclear Magnetic Resonance Log Parameters from Well Log Data Using a Committee Machine with Intelligent Systems

Rohmatul Aminah, M. Dwi Bagus Aurijanto

Indonesian Petroleum Association

..., A., Helle, H.B., 2002, Committee neural networks for porosity and permeability prediction from well logs. Geophysical. Prospects, 50, 645 – 660. Chen...

An Innovative Machine Learning-Based Workflow for Leveraging the Success Ratio of Reservoir Fluid Identification Using Gas while Drilling Data in Mutiara Field, Kutai Basin

Rama Ardhana, Putri Nur, Desianto Payung Battu, Dwi Kurniawan Said, Hendra Halomoan Pasaribu

Indonesian Petroleum Association

... 2024) IBM, (n.d.-b), Neural Networks, available at: https://www.ibm.com/topics/neural-networks (Accessed: 15 January 2024) 15 IBM, (n.d.-c), Random...

Seismic Stratigraphic and Quantitative Interpretation of Leonardian Reefal Carbonates, Eastern Shelf of the Midland Basin: Insight Into Sea Level Effects, Geomorphology and Associated Reservoir Quality; #10909 (2017)

Abidin B. Caf, John D. Pigott

Search and Discovery.com

... (PNN) • Petrophysical Techniques • Integrated Interpretation & Discussion • Conclusions Presenter’s notes: Neural networks can help to enable seismic...

Introduction to Special Issue: Geoscience Data Analytics and Machine Learning

Michael J. Pyrcz

AAPG Bulletin

... computational resources and the development of new algorithms, this is an exciting time for data-driven geoscience. For example, convolutional neural networks...

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Using Distributed Pressure and Temperature Measurements at the Illinois Basin-Decatur Carbon Sequestration Project

Takuto Sakai, Masahiro Nagao, Chin Hsiang Chan, Akhil Datta-Gupta

Carbon Capture, Utilization and Storage (CCUS)

... model for the filtering-based data assimilation process to quantify uncertainty of CO2 leakage. A special type of Recurrent Neural Networks called...

DiffSim: Denoising diffusion probabilistic models for generative facies geomodeling

Minghui Xu, Suihong Song, Tapan Mukerji

International Meeting for Applied Geoscience and Energy (IMAGE)

...). However, the training of GANs may face challenges because two neural networks (the generator and the discriminator) are trained concurrently...