Machine Learning Assisted Lithology Prediction Utilizing Toeplitz Inverse Covariance-Based Clustering (TICC)-Reference-Cited by-同舟云学术

Machine Learning Assisted Lithology Prediction Utilizing Toeplitz Inverse Covariance-Based Clustering (TICC)

Published:2021-11-04 Issue: Volume: Page:
ISSN:
Container-title:Geo-Extreme 2021
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Author:

Bartosik Sean Cristopher¹²,Amirlatifi Amin¹²

Affiliation:

1. Graduate Research Assistant, Swalm School of Chemical Engineering, Mississippi State Univ., Starkville, MS.

2. Assistant Professor, Swalm School of Chemical Engineering, Mississippi State Univ., Starkville, MS. ORCID: https://orcid.org/0000-0002-2225-2087.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784483701.023

Reference22 articles.

1. Al-Khdheeawi E. A. Mahdi D. S. and Feng R. (2019). ‘Lithology determination from drilling data using artificial neural network’ 53rd U.S. Rock Mechanics/Geomechanics Symposium.

2. Azike O. (2011). ‘Multi-Well Real-Time 3D Structure Modeling and Horizontal Well Placement: An Innovative Workflow for Shale Gas Reservoirs’ in SPE Eastern Regional Meeting. Society of Petroleum Engineers. doi: 10.2118/148609-MS.

3. Bartosik S. and Amirlatifi A. (2020). ‘Machine Learning Assisted Geosteering’ in 54th U.S. Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (ARMA). Available at: https://www.onepetro.org/conference-paper/ARMA-2020-1662.

4. Belozerov B. et al. (2018). ‘Automatic well log analysis across priobskoe field using machine learning methods’ Society of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2018 RPTC 2018. doi: 10.2118/191604-18rptc-ru.

5. Uncertainty in structural interpretation: Lessons to be learnt;Bond C. E.;Journal of Structural Geology,2015