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AI-Aided Core Analysis: Faster and Cheaper SCAL StudiesNormal access

Authors: A. Erofeev, D. Orlov and D. Koroteev
Event name: ProGREss'19
Session: Digital Transformation in O&G Exploration / Цифровая трансформация в ГРР
Publication date: 05 August 2019
DOI: 10.3997/2214-4609.201953059
Language: Russian
Info: Extended abstract, PDF ( 637.62Kb )
Price: € 20

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.


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