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...

Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs

J. Cornelio, S. Mohd Razak, A. Jahandideh, Y. Cho, H-H. Liu, R. Vaidya, B. Jafarpour

Unconventional Resources Technology Conference (URTEC)

... on estimating the approximate functional input-output relations by using various statistical and mathematical techniques. Neural Networks are one...

Anti-aliasing seismic data interpolation by dip-informed self-supervised learning

Shirui Wang, Xuqing Wu, Jiefu Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... scheme and often required training datasets with decimatedcomplete pairs to train the deep neural networks. However, considering the field-specific...

Abstract: Understanding Seismic Attributes and Their Use in the Applica-tion of Unsupervised Neural Analysis - Case Histories, Both Conventional and Unconventional, by Deborah Sacrey; #90205 (2014)

Search and Discovery.com

Unconventional Reservoir Microstructural Analysis Using SEM and Machine Learning

Amanda S. Knaup, Jeremy D. Jernigen, Mark E. Curtis, John W. Sholeen, John J. Borer IV, Carl H. Sondergeld, Chandra S. Rai

Unconventional Resources Technology Conference (URTEC)

... specifically Convolutional Neural Networks (CNN), are being used for pixel labeling and feature identification using the CNN U-Net. This network...

High-fidelity GPR image super-resolution via deep-supervised machine learning

Kai Gao, Carly M. Donahue, Bradley G. Henderson, Ryan T. Modrak

International Meeting for Applied Geoscience and Energy (IMAGE)

... imaging may not be the optimal choice. Machine learning uses neural networks to learn the complex mapping function between data and labels, discover...

Field-Scale Bayesian Production Forecasting via Spectral Gaussian-Process Mixtures

Ryan Farell, J. Eric Bickel, Chandrajit Bajaj

Unconventional Resources Technology Conference (URTEC)

... kernels embed Hamiltonian or symplectic structure within neural networks or GP priors—e.g., HNN, SymODEN, SSGP—providing long-term stability and physically...

Enhancing Lithology Classification through a Deep Learning Framework

P. Zhang, T. Gao, R. Li

Unconventional Resources Technology Conference (URTEC)

... algorithms like Support Vector Machines (SVM), Random Forests (RF), and more recently, deep learning architectures including Convolutional Neural Networks...

Characterization and modeling of fault zone fracture swarms and image log fracture interpretations

Abdullah Alhasan, Yahya Qahtani

International Meeting for Applied Geoscience and Energy (IMAGE)

... to populate different properties. Several fracture networks are then modelled using different inputs, including both forward modeling with faults...

Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis

Fan Jiang, Alejandro Jaramillo, Steve Angelovich, Phill Norlund, Julianna Toms

International Meeting for Applied Geoscience and Energy (IMAGE)

...Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis Fan Jiang, Alejandro Jaramillo...

High-resolution prestack seismic inversion of reservoir parameters using an arch network

Ting Chen, Yaojun Wang, Yuan Yuan, Gang Yu, Guangmin Hu

International Meeting for Applied Geoscience and Energy (IMAGE)

...., 2018). Chen et al. (2019) applied iterative deep neural networks (DNN) to inverse seismic wavelet and reflectivity. To make network more effective...

Seismic inversion with implicit neural representations

Juan Romero, Wolfgang Heidrich, Nick Luiken, Matteo Ravasi

International Meeting for Applied Geoscience and Energy (IMAGE)

... representation for inverse problems Implicit neural representation or coordinate-based learning, wherein neural networks directly utilize spatial...

Application of Both Physics-Based and Data-Driven Techniques for Real-Time Screen-Out Prediction with High Frequency Data

Jianlei John Sun, Arvind Battula, Brandon Hruby, Paymon Hossaini

Unconventional Resources Technology Conference (URTEC)

... were not always appropriate for realtime applications. This study uses data-driven methods like deep neural networks along with physics-based approach...

Prescriptive Model for Automatic Online Plunger Lift Unconventional Wells Optimization

Adriana Romero, Christopher Feldmann, Katherine Silva Alonso, Gustavo Martinez, José Barros, Marcelo Montero, Juan Ignacio Alvarez Claramunt, Eugenio Ferrigno

Unconventional Resources Technology Conference (URTEC)

... systems using machine learning. For this purpose, a fault classification model has been developed for plunger lift systems using neural networks focused...

Deep learning seismic full-waveform inversion and transient EM joint inversion for near surface velocity modeling

Daniele Colombo, Ernesto Sandoval-Curiel, Ersan Turkoglu, Weichang Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... et al., 2015). The initial focus has been on the training and optimization of the neural networks before application to the simulated dataset...

3D velocity model building based upon hybrid neural network

Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

...3D velocity model building based upon hybrid neural network Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun 3D velocity model...

Fluid distribution modeling impact on estimating CO2 saturation in Cranfield: A capillary pressure equilibrium approach with invertible neural networks

Sohini Dasgupta, Arnab Dhara, Mrinal K. Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... equilibrium approach with invertible neural networks Sohini Dasgupta* , Arnab Dhara, Mrinal K. Sen, University of Texas at Austin SUMMARY Saturation...