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The AAPG/Datapages Combined Publications Database
Showing 2,441 Results. Searched 200,616 documents.
New Geomechanical and Petrophysical Data from NDI Carrara 1; Implications for Carrara Sub-Basin unconventional Prospectivity
AHE. Bailey, L. Wang, E. Grosjean, CJ. Carson, GA. Butcher, AJM. Jarrett, PA. Henson
Petroleum Exploration Society of Australia (PESA)
... data using both conventional interpretation methods and artificial neural networks. Stratigraphic picks presented in the study are preliminary, are based...
2022
Prediction of Porosity and Fluid Saturation from Full Stack Seismic Data Using Seismic Inversion and Neural Network Analysis, #42254 (2018).
Ahmed S. Ali, Hamed El-Mowafy, Ashraf Ali Hasan, Ali Khairy
Search and Discovery.com
...Prediction of Porosity and Fluid Saturation from Full Stack Seismic Data Using Seismic Inversion and Neural Network Analysis, #42254 (2018). Ahmed...
2018
Background noise suppression for DAS-VSP data using attention-based deep image prior
Yang Cui, Umair bin Waheed, Yangkang Chen
International Meeting for Applied Geoscience and Energy (IMAGE)
... for blind denoising and waveform coherence enhancement in distributed acoustic sensing data: IEEE Transactions on Neural Networks and Learning Systems...
2024
Machine-learning Approach to Optimize Huff-n-Puff Gas Injection in Naturally Fractured Shale Oil Reservoirs
Khaled Enab
Unconventional Resources Technology Conference (URTEC)
... to the system being studied. Artificial Neural Networks (ANNs) and data-driven methods have been successful in achieving this balance by creating fast...
2023
An approach to reduce exploration risk using spectral decomposition, prestack inversion, and seismic facies classification
Carlos Jesus, Wagner Moreira Lupinacci, Patricia Takayama, Joana Almeida, and Danilo Jotta Ariza Ferreira
AAPG Bulletin
...., and T. Kohonen, eds., 1998, Visual explorations in finance: London, Springer Finance, 229 p. Du, K., and M. N. S. Swamy, 2014, Neural networks...
2020
Efficient seismic image super-resolution
Adnan Hamida, Motaz Alfarraj, Abdullatif A. Al-Shuhail, Salam A. Zummo
International Meeting for Applied Geoscience and Energy (IMAGE)
... propose the application of state-ofthe-art Deep Learning based Convolutional Neural Network (CNN) models that are built for image super-resolution (SR...
2022
High-Resolution DFN Modeling via Seismic Attribute Integration in the Sichuan Basin for Completion Optimization
Xuefeng Yang, Shengxian Zhao, Dongchen Liu, Deliang Zhang, Lieyan Cao, Joseph Leines Artieda, Chuxi Liu, Wei Yu, Jijun Miao
Unconventional Resources Technology Conference (URTEC)
... of computing power. Convolutional neural networks (CNNs) have been used for image segmentation with acceptable results delineating faults. Guo et al...
2025
Machine learning applications for frac-hit identification: A field data use case
Xiongjun Wu, Guoxiang Liu, Vyacheslav Romanov
International Meeting for Applied Geoscience and Energy (IMAGE)
.... It utilizes Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to innovatively identify the frac hits due to hydraulic...
2022
Surface-based modeling of 3D architectural elements controlled by near-wellbore modeling
Luis Carlos Escobar Arenas, Patrick Ronnau, Lisa Stright, Steve Hubbard, Brian Romans
International Meeting for Applied Geoscience and Energy (IMAGE)
... neural networks (Titus et al. 2021). Now, we are constraining a 3D model that honors the available data in several scales and perspectives using...
2022
Prestack and poststack seismic amplitude interpretation to support unveiling oil potential of a highly stratigraphic reservoir
S. Mata García, A. Carrillat, W. Torres, P. Cisneros, V. Lucas, P. Bermeo, P. Gonzalez, J. Rodas, C. Miller, P. Zamora, K. Luzuriaga, J. Garrido
International Meeting for Applied Geoscience and Energy (IMAGE)
... by Artificial Neural Networks (ANN) is a robust approach to map thin sandstone reservoir layers embedded in contrasting lithologies and allows for improved...
2023
A Physics-informed Machine Learning Workflow to Forecast Production in a Fractured Marcellus Shale Reservoir
Michael R. Gross, Jeffrey D. Hyman, Shriram Srinivasan, Daniel O’Malley, Satish Karra, Maruti K. Mudunuru, Matthew Sweeney, Luke Frash, Bill Carey, George D. Guthrie, Tim Carr, Liwei Li, Dustin Crandall, Hari Viswanathan
Unconventional Resources Technology Conference (URTEC)
... was accomplished using dfnWorks, a parallelized computational suite that generates 3D discrete fracture networks (DFNs). The DFN honors the subsurface geology...
2021
Mapping Oil-Prone Facies in 3D for Field Development and Optimizing Production: A Midland Basin Case Study
Paritosh Bhatnagar, Venkatesh Anantharamu, Ron Bianco
Unconventional Resources Technology Conference (URTEC)
... density seismic data within the wolfberry section of the Midland Basin. We incorporate supervise deep neural networks to train for saturation facies...
2024
Fracture Diagnostics in Naturally Fractured Formations: An Efficient Geomechanical Microseismic Inversion Model
Meng Cao, Mukul M. Sharma
Unconventional Resources Technology Conference (URTEC)
...Using Neural Networks to Detect Microseismicity and Pick P-wave Arrival Times in Oklahoma. Paper presented at the SEG International Exposition and A...
2022
Abstract: Integrating Well and Seismic Data for Rock Type Prediction Using a Democratic Neural Network Association Approach; #90286 (2017)
Bruno de Ribet
Search and Discovery.com
...Abstract: Integrating Well and Seismic Data for Rock Type Prediction Using a Democratic Neural Network Association Approach; #90286 (2017) Bruno de...
2017
Forecasting Water Production from Oil and Gas Wells Using Machine Learning Models, a Case Study from the Paradox Basin, Utah; #42593 (2024)
Omar Bakelli, Rohit Ramgire, Eric Edelman, Ting Xiao, Brian J. McPherson
Search and Discovery.com
..., including Gradient Boosting, XGBoost, CatBoost, Linear Regression, Support Vector Regression, Decision Trees, Long Short-Term Memory (LSTM) networks...
2024
Pore Pressure Prediction Using Seismic and Well Data with Eaton Method and Neural Network in Carbonate Reservoir, "P" Gas Field, North Sumatra
Panji Satio Hutomo, Farizi Hilman Ramadhan, Rizky Achmad Kurnia, Pradityan Febri Yudhistira, M. Wahdanadi Haidar
Indonesian Petroleum Association
...Pore Pressure Prediction Using Seismic and Well Data with Eaton Method and Neural Network in Carbonate Reservoir, "P" Gas Field, North Sumatra Panji...
2019
Geophysical Corner Articles from September 1996 – present, Compiled
Search and Discovery.com
N/A
2D Seismic and Well Log Data Integration to Predict Pore Pressure Distribution in Brilian Field, Bintuni Basin
Refsi Dian Paparezzi, Warto Utomo, Muhammad Thariq Almuqtadir
Indonesian Petroleum Association
... log of formation target. Neural networks can improve porosity prediction. In simple terms, the research process is described in a flow chart (Figure 5...
2022
A Comparative Analysis of Machine Learning Techniques for Geothermal Wells Drilling Rate of Penetration (ROP) Prediction
Taha Yehia, Moamen Gasser, Hossam Ebaid, Nathan Meehan, Esuru Rita Okoroafor
Unconventional Resources Technology Conference (URTEC)
..., artificial neural networks and support vector machine algorithms demonstrate comparatively lower performance, with MAE ranging from 4.7 to 6.3 and R2 from...
2024
ABSTRACT: Determining Petrophysical Properties and Gas Content in the Barnett Shale Using a Log-based Neural Network Solution
Lee Utley
Fort Worth Geological Society
...ABSTRACT: Determining Petrophysical Properties and Gas Content in the Barnett Shale Using a Log-based Neural Network Solution Lee Utley 2003...
2003
ABSTRACT: Physical Properties of Reservoirs Using an Artificial Neural Network Approach: Example from the Jeanne d'Arc Basin, Eastern Offshore Canada, by Zehui Huang and Mark A. Williamson; #91019 (1996)
Search and Discovery.com
1996
Abstract: Geostatistical Quantification of Geological Information for a Fluvial-Type North Sea Reservoir, by Jef K. Caers; #90914(2000)
Search and Discovery.com
2000
ABSTRACT: Three Dimensional Seismic Inversion and Neural Net Impedance Facies Map in Reservoir Interpretation, by S. K. Addy and J. Hallin; #90908 (2000)
Search and Discovery.com
2000