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

Journal Article · · Expert Systems with Applications
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Not provided.

Research Organization:
Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0020675
OSTI ID:
1853667
Journal Information:
Expert Systems with Applications, Vol. 177, Issue C; ISSN 0957-4174
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

References (12)

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems journal September 2011
Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks journal April 1985
Shear-Wave Velocity Estimation in Porous Rocks: Theoretical Formulation, Preliminary Verification and Applications1 journal February 1992
The effects of porosity and clay content on wave velocities in sandstones conference March 2012
Shear and compressional logs derived from nuclear logs conference March 2012
An approximation for the Xu‐White velocity model journal September 2002
Prediction of shear wave velocity using empirical correlations and artificial intelligence methods journal June 2014
Data driven model for sonic well log prediction journal November 2018
Dynamic data driven sonic well log model for formation evaluation journal April 2019
Missing well log prediction using convolutional long short-term memory network journal May 2020
Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin, Australia journal February 2007
Quantitative investigation of fracture interaction by evaluating fracture curvature during temporarily plugging staged fracturing journal January 2019

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