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Abstract

Summary

The work is devoted to a tool for lithology prediction using seismic data inversion and a creation of a classifier based on machine learning algorithms. There was found a hidden connection between the input data seismic interpretation results: P-Impedance, Vp/Vs and NTG and with high precision predict the probability of the water and oil saturated layers in the understudied and unexplored field sectors.

There were held multiple express tests of different machine learning classification algorithms from examples (labeled data): based on boosting - Gradient Boost, XGBoost, Cat Boost, bagging - Random Forest; Support Vector Machines (SVM), K-Nearest Neighbors (KNN). The best methods: Gradient Boost, XGBoost, Cat Boost were studied more carefully. As a result there was found a method based on machine learning algorithm (Boosting) that allows evaluating data, building a model and then forecasting with a high precision sand and shale arrangement in undrilled and poorly studied zones. This method could be applied when there is hardly distinguishable shales and sand as the insufficient contrast of elastic inversion data. We showed that this method that exhibit relatively high classification accuracy and allows to classify lithotypes: sands, shales and build maps of the sand probability.

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/content/papers/10.3997/2214-4609.202032010
2020-11-30
2024-03-29
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References

  1. 1.Avseth, P., Toxopeus, G., & Ødegaard, E. [2008]. Validating Models By Geologic And Seismic Modeling For Reducing Risk In Global Exploration - A Case-study From the NCS. Society of Exploration Geophysicists, Las Vegas 2008 Annual Meeting
    [Google Scholar]
  2. 2.GollapudiS. [2016] Practical Machine Learning, Packt Publishing, Birmingham, UK, 1–433
    [Google Scholar]
  3. 3.ShubinA.V.. [2014] A technique for studying complex natural reservoirs based on petroelastic modeling and inversion of seismic data, dis. ... kand. those. Sciences, Russian State University of Oil and Gas Gubkina, Moscow, 1–146
    [Google Scholar]
  4. 4.ZakiM.J., MeiraW.Jr. [2014] Data mining and analysis: Fundamental Concepts and Algorithms, Cambridge University Press, Cambridge, 1–604
    [Google Scholar]
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