Solving seismic inverse problems by an unsupervised hybrid machine-learning approach

Mrinal K. Sen, Arnab Dhara

International Meeting for Applied Geoscience and Energy (IMAGE)

... and mathematicians revolutionized the concepts and applicability of the neural networks (NN) in many areas of science and engineering. This resurgence...

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A Novel Method of Automatic Training Data Selection for Estimating Missing Well Log Zone Using Neural Networks; #41055 (2012)

Yingwei Yu, Douglas Seyler, Michael D. McCormack

Search and Discovery.com

...A Novel Method of Automatic Training Data Selection for Estimating Missing Well Log Zone Using Neural Networks; #41055 (2012) Yingwei Yu, Douglas...

Abstract: Recent Developments in the Use of Big Data, Deep Learning and Artificial Intelligence in Upstream E&P; #90310 (2017)

Susan S. Nash

Search and Discovery.com

..., and identifying sweet spots, fracture networks, geochemical markers, and more. Big Data and new analytics are also used for ranking prospects...

Abstract: From Interpretation to Automation: AI- Driven Innovation in Reservoir Modeling; #91213 (2025)

Abdulmohsen Alali, Ezzedeen Alfataierge, Yousef Alshaheen, Pavel Golikov

Search and Discovery.com

... neural networks and Long Short-Term Memory (LSTM) networks to generate synthetic logs from gamma-ray and drilling parameters, bridging the gap...

Seismic data augmentation for automatic faults picking using deep learning

Nam Pham, Sergey Fomel

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks and semisupervised generative adversarial networks: Geophysics, 85, no. 4, O47–O58, doi: https://doi.org/10.1190/geo2019-0627.1...

Automated velocity model building using Fourier neural operators

Guanghui Huang, Sean Crawley, Ramzi Djebbi, Jaime Ramos-Martinez, Nizar Chemingui

International Meeting for Applied Geoscience and Energy (IMAGE)

... efficiently computed in the Fourier domain. We show the advantages of using global FNOs over conventional convolutional neural networks (CNN), to achieve...

Explainable AI: Can neural networks recognize first arrivals after wave separation?

Yanwen Wei, Zhenyu Zhu, Jicai Ding, Yichuan Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

...Explainable AI: Can neural networks recognize first arrivals after wave separation? Yanwen Wei, Zhenyu Zhu, Jicai Ding, Yichuan Wang Explainable AI...

Application of transfer learning and multi-scale feature fusion in intelligent suppression of seismic random noise

Xin Xu, Wuyang Yang, Xinjian Wei, Haishan Li, Nang Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Key Laboratory of lnternet of Things,CNPC Summary Denoising Convolutional Neural Networks (DnCNN), a data-driven learning algorithm, has been widely...

Machine learning inversion of time-lapse three-axis borehole gravity data for CO2 monitoring

Taqi Alyousuf, Yaoguo Li, Richard Krahenbuhl

International Meeting for Applied Geoscience and Energy (IMAGE)

...y data and changes in density, that satisfy the governing equations in the fluid flow simulator. We use a the trained neural networks to map the thre...

Flow Control System Design Using Sliding Mode Control (SMC) with Neural Network in Backloading at Terminal BBM P.T. Pertamina Perak Surabaya

Helmy Yunan Ihnaton, Mariyanto, Imam Abadi, R. Muhsin Budiono

Indonesian Petroleum Association

..., 1, 1-10. K.S. Narendra., K. Parthasar athy, 1990, Identification and Control of Dynam ical Systems Using Neural Net works: IEEE Trans. Neural Networks...

A Fiber-optic Assisted Multilayer Perceptron Reservoir Production Modeling: A Machine Learning Approach in Prediction of Gas Production from the Marcellus Shale

Payam Kavousi Ghahfarokhi, Timothy Carr, Shuvajit Bhattacharya, Justin Elliott, Alireza Shahkarami, Keithan Martin

Unconventional Resources Technology Conference (URTEC)

... production from the MIP-3H. Artificial neural networks (ANN) have been of increasing popularity because of their capabilities in efficiently recognizing...

Seismic Facies Segmentation Using Deep Learning; #42286 (2018)

Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil

Search and Discovery.com

....(2015). One of the earliest works to use neural networks for seismic facies classification was presented by West et al. (2002). The authors combine...

Geologic Characterization for the U.S. SECARB Anthropogenic Test; Combining Modern and Vintage Well Data to Predict Reservoir Properties; #41156 (2013)

Shawna R. Cyphers, Hunter Jonsson, and George J. Koperna, Jr.

Search and Discovery.com

... (modeling) predictions. Study Area Structural Contour Map of Citronelle Dome Background: What are Artificial Neural Networks? Digitized vintage logs...

Development of deep learning method for automatic seismic first break picking

Albert Farkhutdinov, Ruslan Malikov, Izat Shahsenov

International Meeting for Applied Geoscience and Energy (IMAGE)

... by application of neural networks trained on synthetic seismic data that comprehensively mimics and describes the target real data. The effectiveness...

Time-lapse matching of OBN seismic data using 2D convolutional neural networks

Ramon C. F. Araújo, Gilberto Corso, Samuel Xavier-de-Souza, João M. de Araújo, Tiago Barros

International Meeting for Applied Geoscience and Energy (IMAGE)

...Time-lapse matching of OBN seismic data using 2D convolutional neural networks Ramon C. F. Araújo, Gilberto Corso, Samuel Xavier-de-Souza, João M. de...

A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Unconventional Resources Technology Conference (URTEC)

... the model in more detail and a series of examples to demonstrate its application to field data. Methods Artificial neural networks are abstract...

Abstract: Prediction of the Total Organic Carbon Using Artificial Neural Networks and the Spectral Gamma-Ray Logs; #91204 (2023)

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Ashraf Ahmed

Search and Discovery.com

...Abstract: Prediction of the Total Organic Carbon Using Artificial Neural Networks and the Spectral Gamma-Ray Logs; #91204 (2023) Ahmed Abdulhamid...

Fracture Modeling in Petrel

Daniel Rivas

Search and Discovery.com

... with fracture zones, and Neural Networks, which is able to create 3D properties based on well data or well+seismic data. Some other workflows are based...

Fracture Modeling in Petrel

Daniel Rivas

Search and Discovery.com

... with fracture zones, and Neural Networks, which is able to create 3D properties based on well data or well+seismic data. Some other workflows are based...

Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement

Kirill Gadylshin, Ilya Silvestrov, Andrey Bakulin

International Meeting for Applied Geoscience and Energy (IMAGE)

... is similar to object detection problems in computer vision. Deep neural networks for image classification are used in seismic attributes analysis (Das et...

New Technology to Identify and Characterize Natural Fractures

W. W. Weiss, Abdel Zellou

Four Corners Geological Society

..., plus thickness and lithology, with fracture frequency as defined by production. Neural networks are well suited to handling multiple parameter...

Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia

Mohammed Farfour, Douglas Foster

International Meeting for Applied Geoscience and Energy (IMAGE)

...-saturated reservoirs from Poseidon field, Offshore Australia. Feedforward Artificial Neural Networks (ANN) are implemented to combine seismic attributes...

HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES

Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov

Journal of Sedimentary Research (SEPM)

...., Swietojanski, P., Clark, S.R., and Armstrong, R.T., 2020, Automated lithology classification from drill core images using convolutional neural networks...

Source location using physics-informed neural networks with hard constraints

Xinquan Huang, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

...Source location using physics-informed neural networks with hard constraints Xinquan Huang, Tariq Alkhalifah Source location using physics-informed...