Neural Network Algorithm on Electrical Submergible Pump (ESP) Design: Abstract

Sudjati Rachmat, Anas Pudji Santoso

Indonesian Petroleum Association

...Neural Network Algorithm on Electrical Submergible Pump (ESP) Design: Abstract Sudjati Rachmat, Anas Pudji Santoso 1996 218 218 A Neural Network...

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

Ahmad Syahputra

Indonesian Petroleum Association

...Oil Saturation Log Prediction Using Neural Network in New Steamflood Area Ahmad Syahputra IPA20-G-307 PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION...

ABSTRACT: Application of Neural Net Technology and Climate-Stratigraphy in Stratigraphic Correlations, by C. Fonseca, M. Schaaf, and C. J. van der Zwan; #90913(2000).

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Integrating U-net with full-waveform inversion for an efficient salt body construction

Abdullah Alaliand, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... of top and base salt using deep convolutional networks: 88th Annual International Meeting, SEG, Expanded Abstracts, 1956–1960, doi: https://doi.org...

Application of Machine Learning for Production Forecasting for Unconventional Resources

Cheng Zhan, Sathish Sankaran, Vincent LeMoine, Jeremy Graybill, Didi-Ooi Sher Mey

Unconventional Resources Technology Conference (URTEC)

... accumulated production is less than 0.2% and the variance is less than 5%. Method Our proposed method is based on recurrent neural networks (RNN), a class...

Implementation of Denoising Diffusion Probability Model for Seismic Interpretation

Fan Jiang, Konstantin Osypov, Julianna Toms

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks, EAGE Digital, Extended Abstracts, doi: https://doi.org/10.3997/2214-4609.202032049. Jiang, F., K. Osypov, J. Toms, 2022...

Well Performance Prediction in Montney Formation Using Machine Learning Approaches

Hamid Rahmanifard, Hamzeh Alimohammadi, Ian Gates

Unconventional Resources Technology Conference (URTEC)

... formation in the province of British Columbia, Canada. Using this database, the performance of artificial neural networks (ANN) with 11 different training...

Using ANN Prediction in Carbonate Reservoir Properties: Implications for Large-Scale Reservoir Correlation

Abdallah Abdelkarim, John Humphrey

Unconventional Resources Technology Conference (URTEC)

... Neural Networks (ANNs) architectures to predict lithological units (members) of a Permian-Triassic carbonate succession using integration of downhole...

Joint inversion of multi-height gravity and vertical gradient via physics-informed neural network

Yinshuo Li, Wenkai Lu, Cao Song

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Recently, deep learning (DL) methods have achieved outstanding performance in various geophysical tasks. Deep convolutional neural networks (CNN...

Automated Analysis of Gridded Geologic Map Data, Susan M. Schrader and Robert S. Balch, #40198 (2006).

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Machine-learning Facilitates Prediction of Geomechanical Properties Directly From SEM Images in Unconventional Plays

Heehwan Yang, Deepak Devegowda, Mark Curtis, Chandra Rai

Unconventional Resources Technology Conference (URTEC)

... (Knaup et al. 2022; Li et al., 2019; Cang et al., 2017). While artificial neural networks (ANN) formed the backbone of several image analyses workflows...

Integrated Petrophysical Evaluation Applied to the Characterization of Shaly-Sand Reservoirs in the Santonian Gas Field, Santos Basin, Brasil; #50503 (2011)

Thamy da Silva and Marcio Coutinho

Search and Discovery.com

...) were used in order to create an electrofacies model. The crossplots (Th x K) and neural networks shows that spectral curves are relevant as input...

Insights using machine learning in predicting faults and horizons: A case study onshore Texas

Dan Ferdinand Fernandez, Mustafa Karer, Richard Hearn, Ryan King, Sunil Manikani, Gavin Menzel-Jones

International Meeting for Applied Geoscience and Energy (IMAGE)

... Texas dataset. By employing ML technology through convolutional neural networks (CNNs) trained on real data we predict multiple layers of faults from...

Gold Targeting of Fixed Wing Aeromagnetic Data Using Structural Complexity, Self-Organizing Map, and Supervised Deep Neural Network Analyses: A Case Study From the Red Lake Camp, Superior Province, Ontario, Canada

Karl Kwan, Jean M. Legault

International Meeting for Applied Geoscience and Energy (IMAGE)

... aeromagnetic data by Supervised Deep Neural Networks (SDNN). The input of additional geoscientific information, including geophysical, geochemical, and remote...

A two-stage deep learning workflow for automated seismic inversion

Haibin Di, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... attempts have been made towards implementing convolutional neural networks (CNNs) and their derivatives into building an optimal non-linear mapping...

Improving fault resolution from multiple angle stacks by latent feature analysis with deep learning

Fan Jiang, Konstantin Osypov

International Meeting for Applied Geoscience and Energy (IMAGE)

... by convolutional neural networks with uncertainty analysis: 92nd Annual International Meeting, SEG/AAPG, Expanded Abstracts, 1709–1713, doi: https://doi.org...

Research on fault-karst reservoir identification method based on deep convolutional network

Zhipeng Gui, Junhua Zhang, Hong Zhang, Dong Chen, Pengbo Yin

International Meeting for Applied Geoscience and Energy (IMAGE)

... methods, including neural networks and spectral decomposition techniques, have made progress in the identification of marine carbonate reservoirs...

Enhancing seismic data quality: A machine learning approach to denoising and signal damage reduction

Mark Roberts, Olga Brusova, Leandro Gabioli, Alejandro Valenciano

International Meeting for Applied Geoscience and Energy (IMAGE)

... over a narrow range of noise conditions. Brusova et al. (2021) demonstrated the potential of using Convolutional Neural Networks (CNNs) to accurately...

Multi-information intelligent decision process for first-break picking

Fei Luo, Lanlan Yan

International Meeting for Applied Geoscience and Energy (IMAGE)

... intelligence technology, on the basis of traditional neural networks(ANN), the emergence of deep learning has opened a new era in the field of seismic data...

Micro transient EM for seismic sand corrections through physics-coupled deep learning

Daniele Colombo, Ersan Turkoglu, Ernesto Sandoval-Curiel, Javier Giraldo-Buitrago

International Meeting for Applied Geoscience and Energy (IMAGE)

... 3: MicroTEM PhyDLI workflow. DL Networks We tested two types of neural networks (NN) consisting of a shallow feedforward, fully connected artificial...

Automated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagation

Harpreet Kaur, Sergey Fomel, Nam Pham

International Meeting for Applied Geoscience and Energy (IMAGE)

... network to learn robust features (Srivastava et al., 2014). It is a computationally efficient method to simulate an ensemble of neural networks...

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton

Unconventional Resources Technology Conference (URTEC)

...Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir Ted...

Transfer Learning Applied to Seismic Images Classification; #42285 (2018)

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

Search and Discovery.com

... are trying to learn is represented by the neural network model and its parameters. Although deep neural networks have provided good results in the facies...

Well Log Prediction Using Machine Learning

Sundeep Sharma

Oklahoma City Geological Society

... were used: Multiple Linear regression, Ridge regression and Lasso regression. More sophisticated methods such as Random Forest and Neural Networks also...

Abstract: Quantifying Karst-filling Anhydrite Distributions from Pore to Log Scales; San Andres Formation, Permian Basin Part 1: Understanding the process, by A. É. Csoma, R. M. Phelps, H. F. El-Sobky, J. Luczaj, R. H. Goldstein, C. Liu, and X. Liu; #120034 (2012)

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