Generating high-quality labels for deep learning CO2 monitoring using local orthogonalization

Shuang Gao, Sergey Fomel, Yangkang Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... Techniques) and supervised machine learning (e.g., support vector machines) Wrona et al. (2018), deep learning (e.g., convolutional neural networks) Li...

Cluster Efficiency-Based Stimulation: Real-Time Quantified Screenout Monitoring and Diversion Evaluation Through a Data Model Approach

Roman Korkin, Abdul Muqtadir Khan

Unconventional Resources Technology Conference (URTEC)

... nature of NSD over time, employing recurrent neural networks is a natural choice. Specifically constructed long short-term memory (LSTM) or gated recurrent...

Abstract: Determining petrophysical properties and gas content in the Barnett Shale using a log-based neural network solution, by Lee Utley; #90010 (2003).

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Development of an Early Miocene Carbonate Formation From Integrated Well Data, Modern Analogs and Geophysical Information: Case Study From Baturaja Formation, South Sumatera Basin, Indonesia

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Development of Probabilistic Production Enhancement Screening Toolbox

I. A. Razak, T. Morillo, M. F. Mustapah

Indonesian Petroleum Association

... a correlation for determining the oil formation volume factor. There was also development of PVT correlations using neural network technique...

Combined Application of Unsupervised and Deep Learning in Absolute Open Flow Potential Prediction: A Case Study of the Weiyuan Shale Gas Reservoir

Lian Wang, Yuedong Yao, Kongjie Wang, Caspar Daniel Adenutsi, Guoxiang Zhao

Unconventional Resources Technology Conference (URTEC)

..., A., Ranjith, R., Aminzadeh, F., 2017. Comparison of shale oil production forecasting using empirical methods and artificial neural networks. In: SPE Annual...

AVA attribute estimation from misaligned seismic gathers using U-Net

Ammar Ghanim, Ricard Durall, Norman Ettrich

International Meeting for Applied Geoscience and Energy (IMAGE)

... to neural network-based alignment methods, like the one proposed by Durall et al. 2023, providing insights into the model's internal interpretation...

Characterization and Modeling of a CO2 Huff n Puff to Predict and Verify EOR Production and CO2 Storage, #80097 (2010)

Damion J. Knudsen, Charles D. Gorecki, Jordan M. Bremer, Yevhen I. Holubnyak, Blaise A. Mibeck, Darren D. Schmidt, Steven A. Smith, James A. Sorensen, Edward N. Steadman, John A. Harju

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... and performing an error-minimizing stochastic multimineral petrophysical and fluid analysis. Neural networks were used to produce matrix permeability...

Assessment of Complex Fracture Networks Effect on Rate Transient Behavior Using Embedded Discrete Fracture Model

Jiazheng Qin, Shiqing Cheng, Wei Yu, Joseph Leines, Kamy Sepehrnoori

Unconventional Resources Technology Conference (URTEC)

.../10.2118/140555-MS Tian, Chuan and Horne, Roland. 2017. Recurrent Neural Networks for Permanent Downhole Gauge Data Analysis. Presented at the SPE Annual Technical...

Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising

Nanying Lan, Kaiheng Sang, Fanchang Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising Nanying Lan, Kaiheng Sang...

Seismic dataset-specific machine learning framework based on pretraining and fine-tuning

Tariq Alkhalifah, Randy Harsuko

International Meeting for Applied Geoscience and Energy (IMAGE)

... machine learning in processing than training neural networks for specific tasks that may or may not transfer well to new data. We introduce a framework...

Amplitude enhancement of far-offset refractions via machine learning

Lurun Su, Han Wang, Jie Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... to achieve the same objective but with much less computational time. A U-Net neural network is designed for training, validation, and testing. The input...

Deep Learning Enhanced Joint Inversion for Mineral Exploration Using Airborne Geophysics: Application in Decorah Area

Yanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiefu Chen, Yueqin Huang

International Meeting for Applied Geoscience and Energy (IMAGE)

..., 2022, Joint 3d inversion of gravity and magnetic data using deep learning neural networks: Second International Meeting for Applied Geoscience...

Deep Net Simulator (DNS): A New Insight into Reservoir Simulation

Shahdad Ghassemzadeh, Maria Gonzalez Perdomo, Manouchehr Haghighi, Ehsan Abbasnejad

Australian Petroleum Production & Exploration Association (APPEA) Journal

... insight into reservoir simulation Another approach is machine learning, particularly artificial neural networks (ANN). These techniques are widely used...

Discovery Science of Hydraulic Fracturing and Shale Fundamentals

Mohamed Mehana, Javier E. Santos, Chelsea Neil, Matthew R. Sweeney, Jeffery Hyman, Satish Karra, Hongwu Xu, Qinjun Kang, James William Carey, George Guthrie, Hari Viswanathan

Unconventional Resources Technology Conference (URTEC)

... to model the transport properties using reinforcement learning. They used MD and LBM datasets to train physics-constrained neural networks. The hydraulic...

Machine learning-based anisotropy characterization of Wolfcamp shales in the Midland Basin

Jaewook Lee, David E Lumley

International Meeting for Applied Geoscience and Energy (IMAGE)

..., such as neural networks, regression tree, support vector machines (SVM), and ensembles of trees. For each model, we separate and train the data into five...

Dual constrained reservoir modeling with geological factors and seismic attributes for exploration stage

Hongmei Luo, Yiran Xing, Changjiang Wang, Zhijing Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... using neural networks from downhole data of a gas hydrate reservoir in the Krishna–Godavari basin, eastern Indian offshore: Geophysical Journal...

Towards flexible demultiple with deep learning

Mario Fernandez, Norman Ettrich, Matthias Delescluse, Alain Rabaute, Janis Keuper

International Meeting for Applied Geoscience and Energy (IMAGE)

... neural networks in exploration seismology: A technical survey: Geophysics, 89, no. 1, WA95–WA115, doi: https://doi.org/10.1190/geo2023-0063.1. Ronneberger...

Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale

Ali Rezaei, Fahd Siddiqui, Birol Dindoruk, M.Y. Soliman

Unconventional Resources Technology Conference (URTEC)

... artificial neural network (ANN) model with a global sensitivity analysis method to present a reduced-order model for addressing these questions. We...

Efficient Bayesian full-waveform inversion using a deep convolutional autoencoder prior

Shuhua Hu, Mrinal K Sen, Zeyu Zhao, Abdelrahman Elmeliegy, Shuo Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... Many studies prove the potential of deep neural networks (DNN) in solving geophysical inverse problems. For example, Chen and Saygin (2021) propose...

Examples of Lithology Models in the Permian Basin from Cased-hole Logging

Richard C. Odom, Gerald P. Hogan, II, Carroll B. Rogers, Josh R. Fairbanks

West Texas Geological Society

..., W.: “Fast Training of Neural Networks for Remote Sensing,” Remote Sensing Reviews, 1994, Vol. 9. Odom, R. C., Bailey, S. M.: “Cased-hole Lithology...

Bayesian variational auto-encoder for seismic wavelet extraction

Ammar Ghanim, Ricard Durall, Norman Ettrich

International Meeting for Applied Geoscience and Energy (IMAGE)

... wavelet based on deep neural networks: 81st Annual International Conference and Exhibition, EAGE, Extended Abstracts, doi: https://doi.org...

Integrated Geological and Geophysical Analysis by Hierarchical Classification: Combining Seismic Stratigraphic and AVO Attributes (Geophysics Paper 12)

Alexis Carrillat, Tanwi Basu, Raul Ysaccis, Shye Aik Chong, Amiruddin Mansor, Martin Brewer

Geological Society of Malaysia (GSM)

..., using methods either based on artificial neural networks (ANN) (McCormack, 1991) or statistics such as Bayesian classification (Sonneland et al. 1994...

AAPG Mid-Continent Section Meeting; - Abstracts, #90309 (2017).

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Predicting Brittleness for Wolfcamp Shales Using Statistical Rock Physics and Machine Learning; #42566 (2021)

Jaewook Lee, David Lumley

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... to these variables for a more predictive model. In this study, we use the multi-layer feedforward neural network based on the Levenburg-Marquardt algorithm...