Robust machine-learning workflow for subsurface geomechanical characterization and comparison against popular empirical correlations

Li, Hao; Misra, Siddharth

doi:10.1016/j.eswa.2021.114942

Title: Robust machine-learning workflow for subsurface geomechanical characterization and comparison against popular empirical correlations

Journal Article · 31 August 2021 · Expert Systems with Applications

DOI: https://doi.org/10.1016/j.eswa.2021.114942 · OSTI ID:1782740

Li, Hao;

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Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0020675

OSTI ID:: 1782740

Journal Information:: Expert Systems with Applications, Journal Name: Expert Systems with Applications Journal Issue: C Vol. 177; ISSN 0957-4174

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (13)

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems Asoodeh, Mojtaba; Bagheripour, Parisa Rock Mechanics and Rock Engineering, Vol. 45, Issue 1 https://doi.org/10.1007/s00603-011-0181-2	journal	September 2011
Prediction of shear wave velocity using empirical correlations and artificial intelligence methods Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami NRIAG Journal of Astronomy and Geophysics, Vol. 3, Issue 1 https://doi.org/10.1016/j.nrjag.2014.05.001	journal	June 2014
Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia Rezaee, M. R.; Kadkhodaie Ilkhchi, A.; Barabadi, A. Journal of Petroleum Science and Engineering, Vol. 55, Issue 3-4 https://doi.org/10.1016/j.petrol.2006.08.008	journal	February 2007
Data driven model for sonic well log prediction Onalo, David; Adedigba, Sunday; Khan, Faisal Journal of Petroleum Science and Engineering, Vol. 170 https://doi.org/10.1016/j.petrol.2018.06.072	journal	November 2018
Quantitative investigation of fracture interaction by evaluating fracture curvature during temporarily plugging staged fracturing Wang, Bo; Zhou, Fujian; Zou, Yushi Journal of Petroleum Science and Engineering, Vol. 172 https://doi.org/10.1016/j.petrol.2018.08.038	journal	January 2019
Dynamic data driven sonic well log model for formation evaluation Onalo, David; Oloruntobi, Olalere; Adedigba, Sunday Journal of Petroleum Science and Engineering, Vol. 175 https://doi.org/10.1016/j.petrol.2019.01.042	journal	April 2019
Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks Li, Hao; Misra, Siddharth IEEE Geoscience and Remote Sensing Letters, Vol. 14, Issue 12 https://doi.org/10.1109/LGRS.2017.2766130	journal	December 2017
Shear-Wave Velocity Estimation in Porous Rocks: Theoretical Formulation, Preliminary Verification and Applications1 Greenberg, M. L.; Castagna, J. P. Geophysical Prospecting, Vol. 40, Issue 2 https://doi.org/10.1111/j.1365-2478.1992.tb00371.x	journal	February 1992
Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks Castagna, J. P.; Batzle, M. L.; Eastwood, R. L. GEOPHYSICS, Vol. 50, Issue 4 https://doi.org/10.1190/1.1441933	journal	April 1985
An approximation for the Xu‐White velocity model Keys, Robert G.; Xu, Shiyu GEOPHYSICS, Vol. 67, Issue 5 https://doi.org/10.1190/1.1512786	journal	September 2002
Shear and compressional logs derived from nuclear logs Iverson, William P.; Walker, Jerry N. SEG Technical Program Expanded Abstracts 1988 https://doi.org/10.1190/1.1892237	conference	March 2012
The effects of porosity and clay content on wave velocities in sandstones Han, De‐hua; Nur, A. SEG Technical Program Expanded Abstracts 1986 https://doi.org/10.1190/1.1893163	conference	March 2012
Missing well log prediction using convolutional long short-term memory network Pham, Nam; Wu, Xinming; Zabihi Naeini, Ehsan GEOPHYSICS, Vol. 85, Issue 4 https://doi.org/10.1190/geo2019-0282.1	journal	May 2020

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