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...

Using Strike Resolved by Neural-Optic Processing of Non-Digital Dipmeter Data to Determine Dip in Complex Structures Penetrated by Deviated Wells

Milt Enderlin, Dick Lenzer

CSPG Special Publications

...Using Strike Resolved by Neural-Optic Processing of Non-Digital Dipmeter Data to Determine Dip in Complex Structures Penetrated by Deviated Wells...

Jointly data and model driven pre-stack inversion of elastic and anisotropy parameters in HTI media

Xin Zhang, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

... into the inversion process. In recent years, data-driven methods based on deep neural networks (DNNs) have rapidly developed in the field of geophysical parameter...

Fracture-Porosity Identification from Anisotropy AVO and Fluid Substitution Modelling in Seismic 2D Data

Septian Prahastudhi, Tenny Octaviani, Ayi Syaeful Bahri, Yulia Putri Wulandari

Indonesian Petroleum Association

... effective neural networks. In PNNs, the weights are calculated using the concept of ‘distance’ in attribute space from a known point to an unknown point...

A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data

Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen

AAPG Bulletin

... integral derivative cumulative production. The performances of these architectures are compared, and the hybrid convolutional neural networks–long...

OpenFWI 2.0: Benchmark Datasets for Elastic Full-waveform Inversion

Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks (CNNs) (Dhara and Sen, 2022; Wu et al., 2021), recurrent networks (Xu et al., 2022; Zhang et al., 2020), and generative adversarial...

Improving total organic carbon estimation for unconventional shale reservoirs using Shapley value regression and deep machine learning methods

Jaewook Lee, David E. Lumley, and Un Young Lim

AAPG Bulletin

... approaches like the Schmoker density log method and shallow neural networks to estimate TOC from seismic data and well logs. The Schmoker method is easy...

Seismic Determination of Dolomitization and Associated Reservoir Quality Using Supervised Machine Learning Techniques: Lower-Middle Permian Carbonates of the Midland Basin; #42565 (2021)

Abidin Berk Caf, John D. Pigott

Search and Discovery.com

... employs Supervised Bayesian Classification and Probabilistic Neural Networks (PNN) on 3D seismic to create an estimation of the most probable distribution...

Feature Detection for Digital Images Using Machine Learning Algorithms and Image Processing

Xiao Tian, Hugh Daigle, Han Jiang

Unconventional Resources Technology Conference (URTEC)

... with the goal of identifying microfractures. Support vector machine, convolutional neural networks, and four pretrained convolutional neural networks were...