Abstract: Seismic Facies Classification Using Bayesian Networks; #90172 (2014)

Gu Yuan, Zhu Peimin, Rong Hui, Hai Yang, Zeng Fanping

Search and Discovery.com

..., Seismic facies classification and identification by competitive neural networks[J]. GEOPHYSICS,68(6): 1984-1999. Alok Porwal,E.J.M.Carranza,M.Hale...

Enhanced Reservoir Characterization for Optimizing Completion Decisions in the Permian Basin Using a Novel Field-Scale Workflow Including Wells with Missing Data

Artur Posenato Garcia, Laura M Hernandez, Archana Jagadisan, Zoya Heidari, Brian Casey, Rick Williams

Unconventional Resources Technology Conference (URTEC)

... logs by combining supervised neural networks with geostatistical analysis on a rock-type basis. We then used an unsupervised neural network method...

Generating geophysical models from text for constructing the dataset of learning-based MT inversion

Yutong Li, Hongyu Zhou, Rui Guo, Maokun Li, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Conditional Generative Adversarial Network (cGANs) is a popular approach. A typical GAN consists of two networks: a generator and a discriminator...

An Effective Physics-Based Deep Learning Model for Enhancing Production Surveillance and Analysis in Unconventional Reservoirs

Yuewei Pan, Ran Bi, Peng Zhou, Lichi Deng, John Lee

Unconventional Resources Technology Conference (URTEC)

... URTeC 145 3 the performances between CLSTM and Fully Connected Neural Networks (FCNNs) (Zhang, et al. 2018). Yet the physics-based interpretation...

Enhancing fiber-optic DAS microseismic event detection in imbalanced data using embedding space optimization

Min Jun Park, Hassan Almomin, Bob Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... when using conventional deep neural networks. This study proposes a novel methodology to address this imbalance. Initially, conventional deep neural...

Oil Sands Reservoir Characterization: A Case Study at Nexen/Opti Long Lake

Laurie Weston Bellman

Search and Discovery.com

... seismic using a neural-network approach3. Wireline logs directly (or indirectly) measure P-wave velocity, S-wave velocity and density. Integrating...

Oil Sands Reservoir Characterization: A Case Study at Nexen/Opti Long Lake

Laurie Weston Bellman

Search and Discovery.com

... seismic using a neural-network approach3. Wireline logs directly (or indirectly) measure P-wave velocity, S-wave velocity and density. Integrating...

Abstract: Advanced Application of LWD Resistivity Images in Delineating Reservoir Dispersion Pattern and High- Resolution Sequences Stratigraphic Analysis: A Case Study from the Krisna Field, Sunda Basin, Offshore, Indonesia; #91209 (2025)

Laila Warkhaida, Ivan Wu, Reza Widiatmo, Sarvagya Parashar, Yessica F. Sthepani, Nikolai Sirait, Pipit Harinursari, Dwi Cahyono, Ifan Rahmansyah, Ardian Aby Santosa

Search and Discovery.com

... a map. SOMs are a form of neural network but are self-trained (normal neural networks are trained on a calibrations curve). The SOM was calibrated so...

Hydrocarbon Prosepecting Using "Quick Look" Bulk Volume Water

Mark H. Franklin

Rocky Mountain Association of Geologists

... quick assessments of movable water, even when porosity logs are not available. By synthesizing GR curves using neural networks, this technique can...

Automatic low-order weak faults detection from carbonate reservoir based on deep learning and ant tracking

Han Wang, Xingwei Wu, Hanqing Wang, Jin Meng, Ji Chang, Tianrui Ye, Yujie Zhou, Dongwei Zhang, Yitian Xiao

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Convolutional neural networks (CNN), as a representative of deep learning, excel in adaptability and computational efficiency, providing new insights...

A Hybrid Machine Learning Workflow for CO2 Huff-n-Puff

Gurpreet Singh, Anuj Gupta, Uchenna Odi, Davud Davudov, Birol Dindoruk, Ashwin Venkatraman, Rabah Mesdour

Unconventional Resources Technology Conference (URTEC)

... approach uniquely combines numerical reservoir simulations with deep neural networks (DNN) to reduce compute cost and facilitate engineering analyses...

3D CNN for channel identification in seismic volume

Haishan Li, Wuyang Yang, Xiangyang Zhang, Xinjian Wei, Xin Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Convolutional Neural Networks (CNNs) are a specialization of the neural networks for data in the form of multiple arrays (LeCun et al., 2015...

Transformer-based network for an efficient ground roll suppression

Randy Harsuko, Omar Saad, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... analyses with widely utilized denoising neural networks are also presented to establish benchmarks for our achieved results. NETWORK ARCHITECTURE Our...

Simultaneous imaging of basement relief and varying susceptibility in deep-learning approach

Zhuo Liu, Yaoguo Li

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Recovering 3D basement relief using gravity data through convolutional neural networks: Journal of Geophysical Research: Solid Earth, 126, e2021JB022611, doi...

Deep learning decomposition for null and active space estimation for thin-bed reflectivity inversion

Kristian Torres, Mauricio D. Sacchi

International Meeting for Applied Geoscience and Energy (IMAGE)

... Convolutional Neural Networks (CNNs), either as direct learned inversion (end-to-end) approaches (ArayaPolo et al., 2018; Mandelli et al., 2019), as learned...

Identification of New Seismic Evidence Regarding Gas Hydrate Occurrence and Gas Migration Pathways Offshore Uruguay, by Juan Tomasini, Héctor de Santa Ana, and Arthur H. Johnson, #80116 (2010)

Search and Discovery.com

Reliable uncertainty estimation for seismic interpretation with prediction switches

Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

.../10.1190/INT-2018-0188.1. Di, H., Z. Wang, and G. AlRegib, 2018b, Deep convolutional neural networks for seismic salt-body delineation: Presented at the AAPG Annual...

Shaking up the Earth: The AI revolution in seismic interpretation

Ryan Williams

GEO ExPro Magazine

... multiple 3D convolutional neural networks (CNNs), Geoteric AI generates an out-of-the-box result that significantly accelerates the starting point...

Artificial Intelligence Integration for Optimal Reservoir Data Analysis and Pattern Recognition

Dr. Leon Hamilton, Dr. Marianne Rauch

Unconventional Resources Technology Conference (URTEC)

... learning and neural networks poised to revolutionize reservoir data analysis and reservoir engineering. With vast amounts of data generated from all...

Advanced Machine Learning Methods for Prediction of Fracture Closure Pressure, ClosureTime, Permeability and Time to Late Flow Regimes From DFIT

Mohamed Ibrahim Mohamed, Dinesh Mehta, Mohamed Salah, Mazher Ibrahim, Erdal Ozkan

Unconventional Resources Technology Conference (URTEC)

... in the data science and machine learning approach have inspired interest in utilizing neural networks, and supervised model, to URTeC 2762 3...

Mineralogical composition and total organic carbon quantification using x-ray fluorescence data from the Upper Cretaceous Eagle Ford Group in southern Texas

Ahmed Alnahwi, and Robert G. Loucks

AAPG Bulletin

... architectures of neural networks: International Journal of Artificial Intelligence and Expert Systems, v. 1, p. 111–122. Liu, X., S. M. Colman, E...

Shale brittleness prediction using machine learning—A Middle East basin case study

Ayyaz Mustafa, Zeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, Shams Kalam, and Rizwan Ahmed Khan

AAPG Bulletin

... and ANFIS Artificial Neural Networks Artificial neural networks (ANNs) are well-known and multipurpose AI techniques used for approximation, modeling...

Petrophysical Interpretation and Reservoir Characterisation on Proterozoic Shales in National Drilling Initiative Carrara I, Northern Territory

Liuqi Wang, Adam H. E. Bailey, Emmanuelle Grosjean, Chris Carson, Lidena K. Carr, Grace Butcher, Christopher J. Boreham, Dave Dewhurst, Lionel Esteban, Chris Southby, Paul A. Henson

Australian Petroleum Production & Exploration Association (APPEA) Journal

... and inorganic geochemical properties. Artificial neural networks were then applied to interpret the mineral compositions, porosity and permeability from well...

CMP domain near-surface velocity model building based on deep learning

Yihao Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... Abstracts, 1512–1517, doi: https://doi.org/10.1190/segam2017-17627643.1. Röth, G., and A. Tarantola, 1994, Neural networks and inversion of seismic data...

S-wave velocity prediction using a deep learning scheme and attention mechanism

Gang Feng, Wen-Qin Liu, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... is a crucial step in building neural networks. In this study, we calculate the Pearson correlation coefficients to select the logging curves with high...