Machine Learning Applications for a Qualitative Evaluation of the Fracture Network in the Wolfcamp Shale Using Tracer and Completion Data

Abhash Kumar, Chung Yan Shih, Guoxiang Liu, Paul Holcomb, Song Zhao, Richard Hammack, Jeffery Ilconich, Grant Bromhal

Unconventional Resources Technology Conference (URTEC)

...y 1998. Neural Networks: A Comprehensive Foundation, 2nd Edition. Prentice Hall. ISBN 0-13-273350-1.Green, S. and McLennan, J. 5 Aug 2020. Reducin...

Machine Learning-Based Sweet Spot Prediction Method for Canada Tight Sandstone Gas Reservoir

Zan Chen, Yexin Fang, Qian Yuan, Hao Su, Yili Yao, Yu Chen

Unconventional Resources Technology Conference (URTEC)

... to generate images similar to real images using random noise. The GAN proposed by Goodfellow consists of two "adversarial" neural networks...

Semisupervised learning with knowledge embedding for horizon volumes calculation

Rui Guo, He Lin, Maoshan Chen, Chunfeng Tao, Yingnan Gao, Ruochong Wen

International Meeting for Applied Geoscience and Energy (IMAGE)

... facies classification using different deep convolutional neural networks: 88th Annual International Meeting, SEG, Expanded Abstracts, 2046–2050, doi...

Automating the thresholding of multi-stage iterative source separation with priors using machine learning

Nam Pham, Rajiv Kumar, Sunil Manikani, Yousif Izzeldin Kamil Amin, Phillip Bilsby, Massimiliano Vassallo, Tao Zhao

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the convolutional neural networks are built to output the maximum amplitudes of the signal and noise spectrum in the FK domain. There is also a binary...

Nano-enhanced drilling fluids: Effects of MgO and ZnO nanoparticles on rheological properties and ANN modeling for predictive analysis

Moamen Gasser, Taha Yehia, Hossam Ebaid, Nathan Meehan, Omar Mahmoud

International Meeting for Applied Geoscience and Energy (IMAGE)

... on building surfaces using artificial neural networks: Energy and Buildings, 158, 1429–1441, doi: https://doi.org/10.1016/j.enbuild.2017.11.045. Gasser, M...

Seismic Attributes for Reservoir Property Prediction„ A Review (Geophysics Poster 16)

Muhammad Sajid, Zuhar Zahir Bin Tuan Harith

Geological Society of Malaysia (GSM)

.... using rock physics, seismic attributes, and neural networks;acase history'. SEG Technical Program Expanded Abstracts, 18 (1):15721575. YANG, P., YIN, X...

Predicting uniaxial compressive strength using Support Vector Machine algorithm

Hafedz Zakaria, Rini Asnida Abdullah, Amelia Ritahani Ismail, Mohd For Mohd Amin

Geological Society of Malaysia (GSM)

... and construction of algorithms that can learn from and make predictions on data. Commonly known machine learning is Artificial Neural Networks (ANN...

White River Dome Field: Gas Production from Deep Coals and Sandstonesof the Cretaceous Williams Fork Formation

Terrilyn M. Olson

Rocky Mountain Association of Geologists

... and heterogeneous reservoir. Technologies employed in this effort include neural networks for matrix permeability estimation, image log interpretation...

Machine learning-based residual moveout picking

Farhad Bazargani, Wenjun Zhang, Anu Chandran, Zaifeng Liu, Harry Rynja

International Meeting for Applied Geoscience and Energy (IMAGE)

... that is followed by a robust curve fitting step. We show that our deep neural network can effectively learn to replicate human expertise from high...

An integrated machine learning-based fault classification workflow

Jie Qi, Carolan Laudon, Kurt Marfurt

International Meeting for Applied Geoscience and Energy (IMAGE)

...://doi.org/10.1190/geo2018-0646.1. Zhao, T., 2019, 3D convolutional neural networks for efficient fault detection and orientation estimation: 89th Annual...

InvMixer An efficient deep neural network for seismic inversion

Tianyi Zhang, Mauricio Araya-Polo, Anshumali Shrivastava

International Meeting for Applied Geoscience and Energy (IMAGE)

...InvMixer An efficient deep neural network for seismic inversion Tianyi Zhang, Mauricio Araya-Polo, Anshumali Shrivastava InvMixer, an efficient Deep...

Optimization of Relative Geological Time Derived From Flow Field A Label Free Approach

Zhun Li

International Meeting for Applied Geoscience and Energy (IMAGE)

..., and A. Abubakar, 2021, Using relative geologic time to constrain seismic facies classification using neural networks: First International Meeting for Applied...

High precision microseismic phase picking and monitoring based on advanced deep learning

Jiayu Qiao, Jingye Li, Yaru Xue, Wenhua Xu, Yangkang Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

.... In this paper, a convolutional neural network model based on a self-attention mechanism is proposed. It can not only meet the requirement that the input...

Remote Well Site Biostratigraphy and Advances in Automated Fossil Analysis; #41930 (2016)

Gunilla Gard, Iain Prince, Jason A. Crux, J. M. Shin, Bernard Lee

Search and Discovery.com

... to about 1GB without any detectable loss of resolution. Image files are moved from the rigs to Houston offices on company internal networks. Reduces...

A Novel Approach to Productivity Prediction of Carbonate Gas Reservoirs from Electrical Image Logs; #42277 (2018)

Bing Xie, Fu-Sen Xiao, Qiang Lai, Yu-Yu Wu, Da-Li Wang

Search and Discovery.com

... and the porosity from the conventional logs in 25 wells. Figure 7. Productivity prediction model of artificial neural networks. Table 1...

Evaluation of Empirical Correlations and Time Series Models for the Prediction and Forecast of Unconventional Wells Production in Wolfcamp A Formation

Aimen Laalam, Houdaifa Khalifa, Habib Ouadi, Mouna Keltoum Benabid, Olusegun Stanley Tomomewo, Mouad Al Krmagi

Unconventional Resources Technology Conference (URTEC)

..., including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Long-Short-Term Memory (LSTM), Support Vector Machines (SVM), Random Forest...

Accelerating innovation with software abstractions for scalable computational geophysics

Mathias Louboutin, Philipp Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, Felix J. Herrmann

International Meeting for Applied Geoscience and Energy (IMAGE)

...). This allows us to implement deep neural networks in Flux, in which we can combine standard deep learning layers with external third party functions...

Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data

Ruizhi Zhong, Raymond L. Johnson Jr., Zhongwei Chen

Unconventional Resources Technology Conference (URTEC)

... by the biological neural networks that constitute animal brains. The neural network consists of nodes, which are called artificial neurons, to model...

Three-dimensional seismic-based definition of fault-related porosity development: TrentonBlack River interval, Saybrook, Ohio

Justine A. Sagan, Bruce S. Hart

AAPG Bulletin

... Geology, v. 41, p. 150163.Minken, D. A., 2002, A 3-D seismic case study investigating AVO, acoustic inversion, and probabilistic neural networks...

Abstract: Lithofacies Classification and Prediction Using Seismic, Well logs, and Core Data: A Supervised Neural Network Methodology Applied in a Carbonate Formation in Saudi Arabia, by C.G. Macrides and H.H. Soepriatna, #90188 (2014)

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Abstract: Neural Network Application for Frontier Exploration: East/Central African Rift Example; #90174 (2014)

Valentina Baranova, Azer Mustaqeem, Francis Karanja, and Danson Mburu

Search and Discovery.com

...Abstract: Neural Network Application for Frontier Exploration: East/Central African Rift Example; #90174 (2014) Valentina Baranova, Azer Mustaqeem...

A 3D Model of the Unconventional Play in the Goldwyer Formation: An Integrated Shale Rock Characterisation over the Broome Platform, Canning Basin.

Lukman Johnson, Gregory Smith, Reza Rezaee, Ali Kadkhodaie

Unconventional Resources Technology Conference (URTEC)

... authors have utilised intelligent systems such as Artificial Neural Networks, and Neuro-Fuzzy Logic to estimate TOC content from petrophysical well...

Application of Artificial Intelligence Tools for Fault Imaging in an Unconventional Reservoir: A Case Study from the Permian Basin

H. Garcia, L. Plant

Unconventional Resources Technology Conference (URTEC)

... learning tools to image faults pushing the boundary of seismic resolution. URTeC 5534 2 Using 3D networks allows for a voxel-by-voxel, unbiased...

Abstract: Using Strike to Find Dip: Neural-Optic Approach for Processing Non-Digital Dipmeter Data, by M. Enderlin; #90987 (1993).

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Abstract: Using Neural Network Predicted Facies to Refine the Stratigraphic Framework; Maastrichtian Reservoir from Wafra Field, Partitioned Neutral Zone (PNZ), Saudi Arabia and Kuwait, by N. Toomey, H. Tang, and M. W. Scott; #90090 (2009).

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