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The AAPG/Datapages Combined Publications Database
Showing 2,441 Results. Searched 200,599 documents.
Machine Learning Algorithms for Predicting Liquid Loading in Gas Wells; #42583 (2023)
Nassim Bouabdallah, Abdeldjalil Latrach, Aimene Aihar, Adesina Fadairo
Search and Discovery.com
... (SVMs), Random Forests, XGBoost, and Neural Networks, to predict whether the well is loaded or unloaded. We then compared the predictions...
2023
Reliability estimation of the prediction results by 1D deep learning ATEM inversion using maximum depth of investigation
Hyeonwoo Kang, Minkyu Bang, Soon Jee Seol, Joongmoo Byun
International Meeting for Applied Geoscience and Energy (IMAGE)
... neural network is overlaid on the predicted resistivities by the trained ConvNeXt model to provide the guideline of the prediction reliability...
2022
Reconocimiento de Litofacies Aplicando Clasificación Guiada de Datos Sísmicos en un Campo de Crudos Pesados en la Cuenca Oriental de Venezuela [PAPER IN SPANISH] Recognition of Lithofacies using Guided Classification of Seismic Data in a Heavy Oil Field
J. Barreto, A. Espeso, A. Mezones
Asociación Colombiana de Geólogos y Geofisicos del Petróleo (ACGGP)
... for lithofacies prediction trough the use of artificial neural networks methods: A case study from Bloque VIII field, Lake Maracaibo, Venezuela. AAPG...
2006
AI: a Game Changer in Seismic Acquisition and Processing
Matt Deighton, Sverre Olsen
GEO ExPro Magazine
.... Convolutional neural networks (CNNs) are one example of AI that can be trained to perform similar analysis but in an accelerated timeframe. Removing...
2021
Transformer-based deep learning model for accurate rate of penetration prediction in drilling
Carlos Urdaneta, Cheolkyun Jeong, Xuqing Wu, Jiefu Chen
International Meeting for Applied Geoscience and Energy (IMAGE)
... an artificial neural network (ANN) drilling parameter optimization system for improved drilling efficiency and reduced costs by managing bit wear. The system...
2023
Exploring for Deep Gas in the Gulf of Mexico Shelf and Deepwater Using Gas Chimney Processing
David Connolly, Fred Aminzadeh
GCAGS Transactions
.... 9819910.2. Meldahl, P., Heggland, R., Bril, B., and de Groot, P., 2001. Identifying Fault and Gas Chimneys Using Multi-Attributes and Neural Networks...
2003
Technology Explained: Artificial Intelligence Its Use in Exploration and Production Part 2
Barrie Wells
GEO ExPro Magazine
... exclusively with Neural Networks (NN) or Genetic Algorithms. Indeed, Massachusetts Institute of Technology (MIT) defines ML as a subset of AI that is based...
2022
Total Organic Carbon Content Estimation of Bakken Formation, Kevin-Sunburst Dome, Montana using Post-Stack Inversion, Passey (DLogR) Method and Multi-Attribute Analysis
Silas Adeoluwa Samuel, Rui Zhang
Unconventional Resources Technology Conference (URTEC)
... to as “overfitting”. A great statistical technique used in measuring the reliability of non-linear predictions in Neural Networks is CrossValidation...
2021
Evaluation of AI-enhanced processing for automated passive seismic detection and location
Aaron Booterbaugh, Evgeny Naumov
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the application of neural networks to passive seismic monitoring. Some notable examples of this include EQTransformer (Mousavi et al., 2020), PhaseNet (Zhu et al...
2024
Abstract: Quantifying Total Organic Carbon (TOC) from Well Logs Using Support Vector Regression; #90187 (2014)
Yexin Liu, Zhuoheng Chen, Kezhen Hu, and Chris Liu
Search and Discovery.com
... Poor Quality Log Data Using Neural Networks, AAPG Annual Convention, May 2003 2) Beaton, A. P., Pawlowicz, J. G., Anderson, S. D. A., Berhane, H...
2014
Source Rock Reservoir Characterization Using Geology, Geochemical and Drilling Data
Robert Shelley, Amir Mohammad Nejad, Stanislav Sheludko
Unconventional Resources Technology Conference (URTEC)
.... Data modelling techniques such as the use of artificial neural networks (ANNs), can be used to help improve the understanding of what drives production...
2017
Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach
Baosen Zhang, Shiwang Chen, Yitian Xiao, Laiming Zhang, Chengshan Wang
Unconventional Resources Technology Conference (URTEC)
...: Computers & Geosciences, v. 103, p. 111-121. Lima, R. P. D., F. Surianam, K. J. Marfurt, and M. J. Pranter, 2019, Convolutional neural networks as aid...
2021
A flexible and versatile joint inversion framework using deep learning
Yanyan Hu, Jiefu Chen, Xuqing Wu, Yueqin Huang
International Meeting for Applied Geoscience and Energy (IMAGE)
... to improve the joint inversion results iteratively using multiphysics data by combining deep neural networks (DNNs) and the traditional inversion workflow...
2022
Deep compressed learning for 3D seismic inversion
Maayan Gelboim, Amir Adler, Yen Sun, Mauricio Araya-Polo
International Meeting for Applied Geoscience and Energy (IMAGE)
... of binarized neural networks, presented in (Hubara et al., 2016). Let φw ∈ RNs ×1 be the real-valued (non-binary) weights of the trainable sensing layer...
2023
Application of Artificial Intelligence on Black Shale Lithofacies Prediction in Marcellus Shale, Appalachian Basin
Guochang Wang, Yiwen Ju, Timothy R. Carr, Chaofeng Li, Guojian Cheng
Unconventional Resources Technology Conference (URTEC)
...), 437-443. Hsu, C.W., Lin, C., 2002. A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on 13(2), 415-425...
2014
An AI approach to using magnetic gradient tensor analysis for quick depth and property estimation
David A. Pratt, K. Blair McKenzie, Anthony S. White
Petroleum Exploration Society of Australia (PESA)
... systems, machine learning and neural networks. The interest in machine learning and neural networks is high and the attraction of a successful...
2019
Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018)
Haibin Di, Zhen Wang, Ghassan AlRegib
Search and Discovery.com
...Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018) Haibin Di, Zhen Wang, Ghassan AlRegib Deep Convolutional Neural...
2018
Machine Learning Models for Predicting the Rheology of Nanoparticle-Stabilized-CO2-Foam Fracturing Fluid in Reservoir Conditions
Toluwalase Adeola Olukoga, Yin Feng
Unconventional Resources Technology Conference (URTEC)
... temperature and density as input parameters for multiple machinelearning models, including neural networks and support vector machines, to predict CO2...
2021
High-resolution seismic reservoir monitoring with multitask and transfer learning
Ahmed M. Ahmed, Ilya Tsvankin, Yanhua Liu
International Meeting for Applied Geoscience and Energy (IMAGE)
... or hydrocarbon production. This study leverages convolutional neural networks (CNNs), multitask learning (MTL), and transfer learning (TL) to accurately...
2024
Identification and distribution of hydraulic flow units of heterogeneous reservoir in Obaiyed gas field, Western Desert, Egypt: A case study
Mohamed A. Kassab, Ahmed Elgibaly, Ali Abbas, and Ibrahim Mabrouk
AAPG Bulletin
... A. Kassab, Ahmed Elgibaly, Ali Abbas, and Ibrahim Mabrouk 2021 2405 2424 105 12 This study demonstrates the use of neural networks in predicting...
2021
Eagle Ford Fluid Type Variation and Completion Optimization: A Case for Data Analytics
Fahd Siddiqui, Ali Rezaei, Birol Dindoruk, Mohamed Y. Soliman
Unconventional Resources Technology Conference (URTEC)
... analysis was carried out, which revealed patterns and features within the training data. Three separate artificial neural networks (ANNs) were...
2019
Deep learning approach for denoising and resolution enhancement of poststack seismic data
Ruslan Malikov, Tatyana Yusubova, Izat Shahsenov
International Meeting for Applied Geoscience and Energy (IMAGE)
... convolutional neural networks for seismic structural interpretation: Geophysics, 85, no. 4, WA27–WA39, doi: https://doi.org/10.1190/geo2019-0375.1...
2024
Marchenko focusing using convolutional neural networks
Mert S. R. Kiraz, Roel Snieder
International Meeting for Applied Geoscience and Energy (IMAGE)
...Marchenko focusing using convolutional neural networks Mert S. R. Kiraz, Roel Snieder Marchenko focusing using convolutional neural networks Mert...
2022
ANN Method, A New Approach to Find Potential Bypass Zones in Mature Semberah Field, East Kalimantan
Gunna Satria H. Kusumah, Robhy C. Permana, Ridha S. Riadi, Yoseph R. Apranda
Indonesian Petroleum Association
... at Chicago, along with Walter Pitts, a logician in the field of computational neuroscience in 1943. The paper suggested on how the neural networks...
2018
Multi-modal Data-assisted Prediction and Analysis of Shale Gas Production Performance at Early Production Stage in Weirong Shale Gas Field, Sichuan Basin, China
Tianrui Ye, Yitian Xiao, Yong Zhao, Haitao Cao, Chunyan He
Unconventional Resources Technology Conference (URTEC)
... patterns, long-term dependencies, and nonlinear relationships in data through their deep structures and layers of neural networks. Some widely-used deep...
2023