Abstract
Summary The main aim of this work is to study the applicability of Machine Learning (ML) techniques for prediction of rock properties, which are commonly defined via special core analysis (SCAL). The mechanism of SCAL prediction on the basis of routine core analysis (RCA) was developed and validated. The possibility of application of ML methods for estimation of some rock characteristics was demonstrated. The comparative analysis of different ML techniques was provided to choose the most stable and accurate forecast methods. It was shown that Gradient Boosting algorithm and Artificial Neural Network allow to create the most robust and accurate models for considered rock properties.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
