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Point-Cloud Deep Learning of Porous Media for Permeability Prediction [article]

Ali Kashefi, Tapan Mukerji
2021 arXiv   pre-print

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https://web.archive.org/web/20210803000728/https://arxiv.org/pdf/2107.14038v1.pdf

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2021 pre-print arXiv:2107.14038v1 fatcat:hymodk7ntjfwbolhkerqv6wplu
Specifically, we use the classification branch of PointNet and adjust it for a regression task. As a test case, two and three dimensional synthetic digital rock images are considered.  ...  We propose a novel deep learning framework for predicting permeability of porous media from their digital images.  ...  Some of the computing for this project was performed on the Sherlock cluster.  ... 
arXiv:2107.14038v2 fatcat:zqm442abwjf5zpsnq377xe7dxy
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Ali Kashefi, Tapan Mukerji. "Point-Cloud Deep Learning of Porous Media for Permeability Prediction." arXiv (2021)

Seeing permeability from images: fast prediction with convolutional neural networks

Jinlong Wu, Xiaolong Yin, Heng Xiao
2018 Science Bulletin  

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2018 pre-print
arXiv:1809.02996v1 fatcat:5p27mrgknzd6pdh3uog7zihvbm
Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores.  ...  Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential.  ...  The image-permeability pairs form the training database for the neural network based machine learning model.  ... 
doi:10.1016/j.scib.2018.08.006 fatcat:5qsjgergvzfgfdl3i55rv5kgf4
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Jinlong Wu, Xiaolong Yin, Heng Xiao. "Seeing permeability from images: fast prediction with convolutional neural networks." Science Bulletin 63.18 (2018) 1215-1222

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Lukas Mosser, Olivier Dubrule, Martin J. Blunt
2017 Physical review. E  

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2017 pre-print
arXiv:1704.03225v1 fatcat:mx2caihxd5amxf7gibltrl37w4
by three-dimensional image datasets.  ...  Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the  ...  serve as benchmark cases to study the application of GANs to three-dimensional stochastic image reconstruction.  ... 
doi:10.1103/physreve.96.043309 pmid:29347591 fatcat:76paphvqpreblkydnpqwsm6nui
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Lukas Mosser, Olivier Dubrule, Martin J. Blunt. "Reconstruction of three-dimensional porous media using generative adversarial neural networks." Physical review. E 96.4 (2017)

Reconstruction of 3D Porous Media From 2D Slices [article]

Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Boris Belozerov, Evgeny Burnaev, Dmitry Koroteev
2021 arXiv   pre-print

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2019 pre-print arXiv:1901.10233v3 fatcat:jgjvxxclincyrm3hr236zhovnq

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2019 pre-print
arXiv:1901.10233v2 fatcat:2wucavp5hrh7bovcjh6wduu3ve
2019 pre-print
arXiv:1901.10233v1 fatcat:lwblh24bczbdfoilr2lq62ha5u
In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices.  ...  We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples.  ...  Methods Reconstruction of three-dimensional porous media using Generative Adversarial Networks The standard approach for the reconstruction of three-dimensional porous media consists of an application  ... 
arXiv:1901.10233v4 fatcat:s2agmtd6f5gurfw6werssvlcci
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Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Boris Belozerov, Evgeny Burnaev, Dmitry Koroteev. "Reconstruction of 3D Porous Media From 2D Slices." arXiv (2021)

Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

Vanessa Hébert, Thierry Porcher, Valentin Planes, Marie Léger, Anna Alperovich, Bastian Goldluecke, Olivier Rodriguez, Souhail Youssef, J. Schembre-McCabe, H. Ott
2020 E3S Web of Conferences  

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In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training.  ...  Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties.  ...  [13] used a deep learning architecture to instantaneously predict permeability of clastic rocks from high resolution Scanning Electron Microscopy images. Sudakov et al.  ... 
doi:10.1051/e3sconf/202014601003 fatcat:pgcou36opngwzbtzgk2jixjjli
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Vanessa Hébert, Thierry Porcher, Valentin Planes, Marie Léger, Anna Alperovich, Bastian Goldluecke, Olivier Rodriguez, Souhail Youssef, J. Schembre-McCabe, H. Ott. "Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties." E3S Web of Conferences (2020) 01003

The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing

Gaetano Garfi, Cédric M. John, Steffen Berg, Samuel Krevor
2019 Transport in Porous Media  

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For this reason, it is crucial to understand how image processing choices control properties estimation.  ...  It offers an alternative approach to core scale experiments for the estimation of traditional petrophysical properties such as porosity and single-phase flow permeability.  ...  To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.  ... 
doi:10.1007/s11242-019-01374-z fatcat:so2a2zfgufbtdif3htb7gaj2zq
Citation

Gaetano Garfi, Cédric M. John, Steffen Berg, Samuel Krevor. "The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing." Transport in Porous Media 131.3 (2019)

Data-Driven Geothermal Reservoir Modeling: Estimating Permeability Distributions by Machine Learning

Anna Suzuki, Ken-ichi Fukui, Shinya Onodera, Junichi Ishizaki, Toshiyuki Hashida
2022 Geosciences  

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We propose a machine-learning-based method to estimate permeability distributions using measurable data.  ...  In this study, as an initial challenge, we focus on permeability, which is one of the most important parameters for the modeling.  ...  Acknowledgments: We We would like to thank the members in Joint Research program 2021-B-01 and 2018-B-01 for useful discussions.  ... 
doi:10.3390/geosciences12030130 fatcat:pbmgbypdgvgn7oaibt3f2r37zq
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Anna Suzuki, Ken-ichi Fukui, Shinya Onodera, Junichi Ishizaki, Toshiyuki Hashida. "Data-Driven Geothermal Reservoir Modeling: Estimating Permeability Distributions by Machine Learning." Geosciences 12.3 (2022) 130

Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms [article]

Andrei Erofeev, Denis Orlov, Alexey Ryzhov, Dmitry Koroteev
2019 arXiv   pre-print

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The objective of this work is to study the applicability of various Machine Learning algorithms for prediction of some rock properties which geoscientists usually define due to special lab analysis.  ...  However, two hidden layers Neural network has demonstrated the best predictive ability and generalizability for all three rock characteristics jointly.  ...  Conclusion In this paper applicability of various Machine Learning algorithms for prediction of some rock properties were tested.  ... 
arXiv:1902.06525v1 fatcat:irmpxvblmja67msn2i4onejcve
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Citation

Andrei Erofeev, Denis Orlov, Alexey Ryzhov, Dmitry Koroteev. "Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms." arXiv (2019)

Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging

Arne Jacob, Markus Peltz, Sina Hale, Frieder Enzmann, Olga Moravcova, Laurence N. Warr, Georg Grathoff, Philipp Blum, Michael Kersten
2021 Solid Earth  

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This methodology results in a more accurate representation of reservoir rock permeability in comparison to that estimated purely based on µXCT images.  ...  The results prove the applicability of our novel approach by combining FIB-SEM with X-ray tomographic rock core scans to achieve a good correspondence between measured and simulated permeabilities.  ...  We would like to thank Fabian Wilde and the staff of PETRA synchrotron facility at DESY Hamburg for their assistance with the imaging beamline P05.  ... 
doi:10.5194/se-12-1-2021 fatcat:6mwpzkxowjby5hs3jcm77ymcx4
DOAJ Szczepanski
Citation

Arne Jacob, Markus Peltz, Sina Hale, Frieder Enzmann, Olga Moravcova, Laurence N. Warr, Georg Grathoff, Philipp Blum, Michael Kersten. "Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging." Solid Earth 12.1 (2021) 1-14

Deep convolutions for in-depth automated rock typing

E.E. Baraboshkin, L.S. Ismailova, D.M. Orlov, E.A. Zhukovskaya, G.A. Kalmykov, O.V. Khotylev, E. Yu Baraboshkin, D.A. Koroteev
2019 Computers & Geosciences  

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2019 pre-print arXiv:1909.10227v2 fatcat:4gfpeyoifjdzxjtlu5qcosga7a

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2019 accepted
arXiv:1909.10227v3 fatcat:74z3uflof5b2hpapm7e7f3kz5q
2019 pre-print
arXiv:1909.10227v1 fatcat:ubqoolbjg5bzbdtes6ksbxsvdm
We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently.  ...  The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required.  ...  Acknowledgments The authors thank Alexander Vladimirovich Ivchenko (MIPT) for his advice during preparation of the paper.  ... 
doi:10.1016/j.cageo.2019.104330 fatcat:foy6n4zrpjbq5nka4v7si5ylk4
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Citation

E.E. Baraboshkin, L.S. Ismailova, D.M. Orlov, E.A. Zhukovskaya, G.A. Kalmykov, O.V. Khotylev, E. Yu Baraboshkin, D.A. Koroteev. "Deep convolutions for in-depth automated rock typing." Computers & Geosciences (2019) 104330

Inverse Problems: Theory and Application to Science and Engineering 2015

Davide La Torre, Herb Kunze, Franklin Mendivil, Manuel Ruiz Galan, Rachad Zaki
2015 Mathematical Problems in Engineering  

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Acknowledgments The guest editors thank all of the authors as well as all others who submitted papers for consideration. Davide La Torre Herb Kunze Franklin Mendivil Manuel Ruiz Galan Rachad Zaki  ...  First, a three-dimensional numerical simulation is used in the creation of training and testing samples for ELM model construction.  ...  In the paper "Back Analysis of Geomechanical Parameters Using Hybrid Algorithm Based on Difference Evolution and Extreme Learning Machine," by Z.  ... 
doi:10.1155/2015/796094 fatcat:2ugywwg56nhsdhczmtwoeeyrpu
DOAJ Szczepanski
Citation

Davide La Torre, Herb Kunze, Franklin Mendivil, Manuel Ruiz Galan, Rachad Zaki. "Inverse Problems: Theory and Application to Science and Engineering 2015." Mathematical Problems in Engineering (2015) 1-3

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Yan Kuchin, Jānis Grundspeņķis
2017 Applied Computer Systems  

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The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.  ...  An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made.  ...  The application of methods of machine learning to problems for which there is no rigorous mathematical model, and only expert estimates are available, is often the optimal method of solution.  ... 
doi:10.1515/acss-2017-0014 fatcat:62zr6bd6o5cpfbjnr4e2mi2m3e
DOAJ
Citation

Yan Kuchin, Jānis Grundspeņķis. "Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan." Applied Computer Systems 22.1 (2017) 21-27

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

R. Gholami, A. R. Shahraki, M. Jamali Paghaleh
2012 Mathematical Problems in Engineering  

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This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field.  ...  In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine.  ...  Acknowledgment The authors appreciate anonymous reviewers for their constructive comments and contributions in improving the paper.  ... 
doi:10.1155/2012/670723 fatcat:mhqul6tsgjhw7chfz4y5htrora
DOAJ Szczepanski
Citation

R. Gholami, A. R. Shahraki, M. Jamali Paghaleh. "Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine." Mathematical Problems in Engineering (2012) 1-18

Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO2 Geological Sequestration

Suryeom Jo, Changhyup Park, Dong-Woo Ryu, Seongin Ahn
2021 Energies  

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The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of fully-connected  ...  This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920.  ...  Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing is not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14020413 fatcat:ohapxmzzcjdlrk3aibt4at22pm
DOAJ
Citation

Suryeom Jo, Changhyup Park, Dong-Woo Ryu, Seongin Ahn. "Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO2 Geological Sequestration." Energies (2021) 413

Deep learning for lithological classification of carbonate rock micro-CT images [article]

Carlos E. M. dos Anjos, Manuel R. V. Avila, Adna G. P. Vasconcelos, Aurea M.P. Neta, Lizianne C. Medeiros, Alexandre G. Evsukoff, Rodrigo Surmas
2020 arXiv   pre-print

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Therefore, this work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images, thus making possible lithological classification  ...  All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes.  ...  The authors also thank the Brazilian Research Council (CNPq) for the scholarships for students and researchers.  ... 
arXiv:2007.15693v1 fatcat:viww4yfe5ffgdojhlijzgx4hkq
Citation

Carlos E. M. dos Anjos, Manuel R. V. Avila, Adna G. P. Vasconcelos, Aurea M.P. Neta, Lizianne C. Medeiros, Alexandre G. Evsukoff, Rodrigo Surmas. "Deep learning for lithological classification of carbonate rock micro-CT images." arXiv (2020)

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