Data driven model for sonic well log prediction-Reference-Cited by-同舟云学术

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Data driven model for sonic well log prediction

Published:2018-11 Issue: Volume:170 Page:1022-1037
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Onalo David,Adedigba Sunday,Khan Faisal,James Lesley A.,Butt Stephen

Funder

Natural Science and Engineering Research Council of Canada

Canada Research Chair (CRC) Program

Hibernia Management and Development Company (HMDC), Chevron Canada

Innovate NL

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference83 articles.

1. Obtaining both horizontal stresses from wellbore collapse;Aadnoy;SPE/IADC Drill. Conf.,2013

2. Dynamic failure analysis of process systems using neural networks;Adedigba;Process Saf. Environ. Protect.,2017

3. Prediction of shear wave velocity using artificial neural network technique, multiple regression and petrophysical data: a case study in Asmari reservoir (SW Iran);Akhundi;Open J. Geol.,2014

4. Investigating the dependence of shear wave velocity on petrophysical parameters;Al-Dousari;J. Petrol. Sci. Eng.,2016

5. Estimation of the rock mechanical properties using conventional log data in North Rumaila field;Al-Kattan;Iraq. J. Chem. Pet. Eng.,2012

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1. FlexLogNet: A flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing;Computers & Geosciences;2024-09

2. Machine learning assisted Kriging to capture spatial variability in petrophysical property modelling;Marine and Petroleum Geology;2024-09

3. Application of artificial intelligence techniques to predict log at gaps: a case study;Earth Science Informatics;2024-06-07

4. Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism;Applied Sciences;2024-03-15

5. Statistical feature engineering using discrete wavelet transform: a tool for improving the prediction of compressional and shear sonic travel time logs;Petroleum Science and Technology;2023-12-18

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