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Showing 2,441 Results. Searched 200,616 documents.
Seismic data denoising by combining self-supervised and supervised learning
Yen Sun, Paul Williamson
International Meeting for Applied Geoscience and Energy (IMAGE)
... Transactions on Neural Networks and Learning Systems, 34, 3371–3384, doi: https://doi.org/10.1109/TNNLS.2021.3132832. Yao, H., H. Ma, Y. Li, and Q...
2023
Improving Prediction of Total Organic Carbon in Prospective Australian Basins by Employing Machine Learning
Irina Emelyanova, Marina Pervukhina, M. Ben Clennell, David N. Dewhurst
Petroleum Exploration Society of Australia (PESA)
.../d/Minerals_Energy/). the Northern Territory Geological Survey REFERENCES Bishop, M., 2003, Neural networks for pattern recognition: University...
2016
Strike-slip fault skeletonization based on deep learning cascade ant tracking method
Zhipeng Gui, Junhua Zhang, Rujun Wang, Yintao Zhang, Chong Sun, Mei Yang
International Meeting for Applied Geoscience and Energy (IMAGE)
... algorithms to achieve efficient automatic fault identification (Araya et al., 2017). In the same year, Huang et al. used convolutional neural networks (CNN...
2024
Facies-constrained elastic full-waveform inversion for tilted orthorhombic media
Ashish Kumar, Ilya Tsvankin
International Meeting for Applied Geoscience and Energy (IMAGE)
... convolutional neural networks to mitigate the influence of tradeoffs and increase the spatial resolution of FWI. The developed CNN generates a facies model...
2024
Automatic detection of DAS-recorded microseismic fracture reflections
Youfang Liu, Ivan Lim, Chen Ning, Kurt Nihei
International Meeting for Applied Geoscience and Energy (IMAGE)
... The workflow can be further automated through deep learning methods. For instance, we can utilize the deep convolutional neural networks to perform the point...
2024
Unit-constrained data-driven discovery of a wave equation
Shijun Cheng, Tariq Alkhalifah
International Meeting for Applied Geoscience and Energy (IMAGE)
... with evolutionary approach: arXiv preprint arXiv:1903.08011. Raissi, M., P. Perdikaris, and G. E. Karniadakis, 2019, Physics-informed neural networks: A deep...
2024
A prior regularized 3D full-waveform inversion using 2D generative diffusion models
Fu Wang, Tariq Alkhalifah, Xinquan Huang
International Meeting for Applied Geoscience and Energy (IMAGE)
..., we do not use the direct prediction of x via the neural networks. Instead, we use the &-prediction parameterization, defined q 1 U8 ¯ as (\, x 8...
2024
Predicting Coiled-Tubing Drilling Dynamics Using Transformers
Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
Unconventional Resources Technology Conference (URTEC)
... prediction for geothermal drilling at Utah FORGE, utilizing machine learning models like random forest regressors and artificial neural networks. Hegde et al...
2024
Completion Optimization While Drilling Geomechanical Steering Towards Fracable Rock Using Corrected Mechanical Specific Energy
A. Jacques, A. Ouenes, R. Dirksen, M. Paryani, S. Rehman, M. Bari
Unconventional Resources Technology Conference (URTEC)
... by Lehman et al (2016). The use of neural networks to correlate drilling data to geomechanical logs requires nearby training wells drilled in a similar...
2017
De-risking Saline Aquifer-Type CO2 Storage Resources Via Machine Learning-based Reservoir Modelling. Case Study, Bunter Sandstone Formation, Southern North Sea
Edwin Tillero, Jose L. Mogollon, Francisco Tillero
Carbon Capture, Utilization and Storage (CCUS)
... (ML) approach, particularly artificial neural networks (ANN), which have proven to be efficient tools to relate inputs and outputs from a high...
2024
Automatic facies classification using convolutional neural network for three-dimensional outcrop data: Application to the outcrop of the mass-transport deposit
Ryusei Sato, Kazuki Kikuchi, and Hajime Naruse
AAPG Bulletin
...., 2016). The SGD is a standard optimization method used to train neural networks, which divides the training data into small subsets called minibatches...
2025
Multi-Level of Fracture Network Imaging: A HFTS Use Case and Knowledge Transferring
Guoxiang Liu, Abhash Kumar, Song Zhao, Chung Yan Shih, Veronika Vasylkivska, Paul Holcomb, Richard Hammack, Jeffery Ilconich, Grant Bromhal
Unconventional Resources Technology Conference (URTEC)
... Resources Technology Conference. https://doi.org/10.15530/urtec-20201544 Haykin, S.O. 1 July 1998. Neural Networks: A Comprehensive Foundation, 2nd Edition...
2022
Developments Relating Total Organic Carbon Conversion in Unconventional Reservoirs to 3D Seismic Attributes
Nancy House, Janell Edman
Unconventional Resources Technology Conference (URTEC)
... waves travel through the rock. Equally important, the escalation in computing power via methods such as machine learning, neural networks...
2019
Machine Learning Regressors and their Metrics to predict Synthetic Sonic and Brittle Zones
Ishank Gupta, Deepak Devegowda, Vikram Jayaram, Chandra Rai, Carl Sondergeld
Unconventional Resources Technology Conference (URTEC)
...), neutron porosity (NPHI) and shale volume (Vsh) from gamma ray and subsequently use neural networks to generate synthetic sonic logs using the conventional...
2019
Predicting ground surface deformation induced from CO2 plume movement using machine learning
Ibrahim M. Ibrahim, Saeed Salimzadeh, Dane Kasperczyk, Teeratorn Kadeethum
Australian Energy Producers Journal
.... This framework consists of two neural networks in competition: a generator that aims to produce fake but realistic-looking data, and a discriminator whose task...
2024
A Prospectivity Checklist for Unconventional Plays; #70160 (2014)
Susan Smith Nash
Search and Discovery.com
... analytical techniques (data mining, cluster analysis, neural networks) to uncover relationships that lead to possible areas of prospectivity? Ranking o...
2014
Probabilistic Modeling of Well Interference for Shale and Tight Development with Subsurface Uncertainty
Yuguang Chen, Ryan Burke, Tom Tran, Andrew Roark, Scott Hanson
Unconventional Resources Technology Conference (URTEC)
... modeling stage ‒ hydraulic fracture simulation (Planar 3D and Unconventional Fracture Model) to obtain an ensemble of hydraulic fracture networks...
2024
Training data versus deep learning architectures in the seismic fault attribute computation
Bo Zhang, Yitao Pu, Zhaohui Xu, Naihao Liu, Shizhen Li, Fangyu Li
International Meeting for Applied Geoscience and Energy (IMAGE)
... to train convolutional neural networks for seismic structural interpretation: Geophysics, 85, no. 4, WA27–WA39, doi: https://doi.org...
2022
Regional Eagle Ford Modeling: Integrating Facies, Rock Properties, and Stratigraphy to Understand Geologic and Reservoir Characteristics
David Hull, Philip Chapman, Dave Miller, David Ingraham, Nicole Fritz, Nicholas Kernan
Unconventional Resources Technology Conference (URTEC)
.... In the bioclastic marl facies, TOC averages 2-3 percent and porosity averages 8-10 percent. This suggests different pore networks in each facies...
2015
Mechanistic Understanding and Data-driven Prediction of Liquid Loading in Long-lateral Oil Wells in Unconventional Reservoirs
Xiao Zhang, Amit Kumar, Tyler Nahhas, Willy Manfoumbi, Christopher Frazier, Gunta Chomchalerm, Yang Chen, Isara Tanwattana, Tamas Toth, Huafei Sun, Aaron Shinn, Peng Xu
Unconventional Resources Technology Conference (URTEC)
... models, including tree-based algorithms (random forest and gradient boosting), feed-forward neural networks, and recurrent neural networks (simple RNN...
2023
Indirect Estimation of Fluid Transport and Rock Mechanical Properties from Elemental Compositions: Implications for Sweet Spot Identification in the Montney Formation (Canada)
A. Ghanizadeh, B. Rashidi, C.R. Clarkson, A. Vahedian, C.P. Vocke, A.R. Ghanizadeh
Unconventional Resources Technology Conference (URTEC)
... contents. Advanced statistical methods (Artificial Neural Network) are employed to quantify these relations and develop algorithms for indirect estimation...
2017
Lithologic Prediction from Seismic Attributes in the Balcon Field, Colombia
J. E. Calderon
Asociación Colombiana de Geólogos y Geofisicos del Petróleo (ACGGP)
... multilinear transforms and neural networks. LOCATION AND GEOLOGICAL SETTING The study area is located in the western Neiva sub-basin within the Upper...
2004
Sequential Simulation for Modeling Geological Structures from Training Images
S. B. Strebelle
AAPG Special Volumes
.... The methodology proposed appears to be easy to apply, general, and fast. Caers, J., and A. Journel, 1998, Stochastic reservoir simulation using neural networks...
2006
The Power of Predictive Analytics in Oil Field Development: Integrating Machine Learning with Reservoir Hydrocarbon Data to Enable Enhanced Oil Recovery of Hugin Formation within the Theta Vest Structure
Lilik T. Hardanto, Fenny Chrisman
Indonesian Petroleum Association
... bias associated with each network. DNNA uses cross-validation and bootstrap error values. It comprises different neural networks which having various...
2021
Time-Dependent Performance Evaluation of Cyclic Injection of Gas Mixtures into Hydraulically-Fractured Wells in Appalachian Sandstones; #11009 (2017)
Emre Artun
Search and Discovery.com
... the cost of generation is lower than other gases. Results were also used to develop a screening model that is based on neural networks that forecasts...
2017