Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators

Ziyi Yin, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann

International Meeting for Applied Geoscience and Energy (IMAGE)

... There exists a growing literature on solving numerical PDEs via learned data-driven approaches involving neural networks (Lu et al., 2019; Raissi et al...

Extracting High Fidelity Geological Information From 3D Seismic and Well Data

Marianne Rauch

Unconventional Resources Technology Conference (URTEC)

... seismic gathers. Traditionally, rock properties are estimated from elastic inversion results using neural networks. This article discusses a method...

Abstract: From Images and Open Hole Logs to Sequence Recognition and Sedimentary Environments, by I. Le Nir and G. Ruiz; #90923 (1999)

Search and Discovery.com

A denoising diffusion probabilistic modeling (DDPM) approach for predicting CO2 plume evolution from seismic shot gathers

Alexander Y. Sun, Zi Xian Leong, Tieyuan Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

... approximate it using neural networks, 2 𝑝𝑝 𝜃𝜃 (𝑥𝑥 𝑡𝑡−1 |𝑥𝑥 𝑡𝑡 ) = N(𝑥𝑥 𝑡𝑡−1...

Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land

Francisco J. Parga Garcia, Jie Fang, Niven Shumaker

Unconventional Resources Technology Conference (URTEC)

...Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land Francisco J. Parga Garcia, Jie Fang, Niven Shumaker URTeC...

Up-Scaled Petrophysical Analyses Using Micro-Level Field-Of-View Petrographic Images for the Kapuni Group, Taranaki Basin, New Zealand, #10953 (2017).

Aamer Alhakeem, Kelly Liu, Waleed H. Al-Bazzaz

Search and Discovery.com

... are then processed to study the pore networks in the space domain between a lower bound of 50 μm and an upper bound of 4 mm. Then, petrophysical parameters...

Seismic reflectivity inversion via a regularized deep image prior

Hongling Chen, Mauricio D. Sacchi, Jinghuai Gao

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–18. Second International Meeting for Applied Geoscience...

Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning

Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles

Australian Petroleum Production & Exploration Association (APPEA) Journal

...). Success has been demonstrated in applying neural networks to verify the efficacy of pre-stack data processes (Bekara and Day 2019), but this has...

Application of interactive convolutional neural network micro-fracture prediction technology based on prestack depth migration data in deep shale gas reservoirs

Xiaolan Wang, Furong Wu, Junfeng Liu, Dianguang Zang, Xiao Yang, Yangjing Li, Xiaoyan Cheng

International Meeting for Applied Geoscience and Energy (IMAGE)

... Neural Prediction Technology Network (CNN) Fracture Convolutional neural networks are a type of deep learning model specifically designed...

Abstract: AI-Driven Seismic Reservoir Characterization; #91210 (2025)

Hesham Moubarak

Search and Discovery.com

...’s hybrid application of Multivariate Geostatistics Attributes (MAT), Probabilistic Neural Networks (PNN), and other neural network variants (Fig. 08, Fig...

3D real-time imaging for electromagnetic fracturing monitoring based on deep learning

Zhigang Wang, Yao Lu, Ying Hu, Yinchu Li, Ke Wang, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... learning electromagnetic inversion with convolutional neural networks: Geophysical Journal International, 218, 817–832, doi: https://doi.org...

Residual Saturation During Multiphase Displacement in Heterogeneous Fractures with Novel Deep Learning Prediction

Eric Guiltinan, Javier E. Santos, Qinjun Kang

Unconventional Resources Technology Conference (URTEC)

... present a machine learning technique based on deep neural networks to predict the fluid distribution within these fractures at steady state trained upon...

Abstract: Application of Fuzzy Interference Systems for Prediction of the Total Organic Carbon; #91204 (2023)

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny

Search and Discovery.com

... artificial neural networks (ANN) and it predicts the TOC from the GR, deep resistivity (DR), sonic transit time (DT), and formation bulk density (RHOB). When...

Integrating U-net into full-waveform inversion for salt body building: A challenging case

Sixiu Liu, Abdullah Alali, Shijun Cheng, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... regularization. Recently, neural networks (NNs) have been extensively applied to perform many seismic tasks, such as denoising (Birnie and Ravasi...

Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the well

Paige Given, Fantine Huot, Ariel Lellouch, Bin Luo, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, Kurt Nihei

International Meeting for Applied Geoscience and Energy (IMAGE)

...Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation...

MFF net: A multiscale feature fusion network for electromagnetic and seismic joint inversion

Yonghao Wang, Zhuo Jia, Yinshuo Li, Wenkai Lu

International Meeting for Applied Geoscience and Energy (IMAGE)

.... This approach harnesses the robust nonlinear fitting capacities of neural networks to optimize the objective function efficiently. Furthermore...

Date-driven seismic velocity inversion via deep residual U-net

Yiran Huang, Chuang Pan, Qingzhen Wang, Jun Li, Jianhua Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion approaches based on deep neural networks are chosen as an alternative strategy for FWI (Adler et al., 2021; Mousavi et al., 2024). As we know...

Predicting gas migration through existing oil and gas wells

James A. Montague, George F. Pinder, and Theresa L. Watson

Environmental Geosciences (DEG)

... through learning rate adaptation: Neural Networks, v. 1, no. 4, p. 295–307, doi:10.1016/0893-6080(88)90003-2. Kang, M., C. M. Kanno, M. C. Reid, X...

Seismically Driven Fractured Reservoir Characterization (Paper 4)

Abdel M. Zellou, Ahmed Ouenes, Gary Robinson, Udo Araktingi

Geological Society of Malaysia (GSM)

... on Geomechanics and Geologic Data Uncertainties. ECMOR IX, Cannes, France Ouenes, A. (2000): Practical application of fuzzy logic and neural networks to fractured...

Abstracts: Automatic P-wave Arrival Time Picking Method for Seismic and Microseismic Data; #90173 (2015)

Jubran Akram

Search and Discovery.com

... that are based on the correlation properties, on some statistical criteria or on artificial neural networks for both individual and group of traces...

Seismic interpolation based on quadratic denoising neural network

Yuhan Sui, Xiaojing Wang, Jianwei Ma

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic interpolation based on quadratic denoising neural network Yuhan Sui, Xiaojing Wang, Jianwei Ma Seismic interpolation based on quadratic...

A cross-shape deep Boltzmann machine for petrophysical seismic inversion

Son Dang Phan and Mrinal K. Sen

AAPG Bulletin

... such as the Hopfield neural networks (Hopfield, 1982) have been used for velocity analysis (Calderon-Macias and Sen, 1993), seismic migration (Vamaraju...

Practical application of neural networks in assessing completion effectiveness in the Montney unconventional gas play in northeast British Columbia, Canada

Jason Cai, John Cole, Alan Young

CSPG Bulletin

...Practical application of neural networks in assessing completion effectiveness in the Montney unconventional gas play in northeast British Columbia...

Seismic diffractions separation and imaging based on convolutional neural network

Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

.../10.1190/geo2010-0229.1. Di, H., Z. Wang, and G. AlRegib, 2018, Deep convolutional neural networks for seismic salt-body delineation: AAPG ACE. Fomel, S., E...

Quantifying Fracture and Reservoir Properties in Unconventional Reservoir from Production Data: A Case Study of Shale Gas Well

Sutthaporn Tripoppoom, Wei Yu, Jijun Miao,

Unconventional Resources Technology Conference (URTEC)

... and surrounding fracture networks. The neural network Markov chain Monte Carlo (NN-MCMC) algorithm was used to achieve automated history matching...