Patents by Inventor Stanislav Glubokovskikh

Stanislav Glubokovskikh has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Determination of elastic properties of a geological formation using machine learning applied to data acquired while drilling
    Patent number: 11781416
    Abstract: Methods for determination of elastic properties of geological formations using machine learning include extracting a first feature vector from data acquired during drilling. The data includes at least drilling parameters. The first feature vector is indicative of a drilling environment classification. A machine learning classification algorithm determines the drilling environment classification based on the first feature vector. A machine learning regression algorithm is selected from multiple machine learning regression algorithms based on the drilling classification. A second feature vector is extracted from the data acquired during drilling based on the drilling classification and the selected machine learning regression algorithm. The second feature vector is indicative of elastic properties of a geological formation. The selected machine learning regression algorithm determines the elastic properties of the geological formation based on the second feature vector.
    Type: Grant
    Filed: October 16, 2019
    Date of Patent: October 10, 2023
    Assignee: Saudi Arabian Oil Company
    Inventors: Andrey Bakulin, Robert Smith, Stanislav Glubokovskikh
  • DETERMINATION OF ELASTIC PROPERTIES OF A GEOLOGICAL FORMATION USING MACHINE LEARNING APPLIED TO DATA ACQUIRED WHILE DRILLING
    Publication number: 20210140298
    Abstract: Methods for determination of elastic properties of geological formations using machine learning include extracting a first feature vector from data acquired during drilling. The data includes at least drilling parameters. The first feature vector is indicative of a drilling environment classification. A machine learning classification algorithm determines the drilling environment classification based on the first feature vector. A machine learning regression algorithm is selected from multiple machine learning regression algorithms based on the drilling classification. A second feature vector is extracted from the data acquired during drilling based on the drilling classification and the selected machine learning regression algorithm. The second feature vector is indicative of elastic properties of a geological formation. The selected machine learning regression algorithm determines the elastic properties of the geological formation based on the second feature vector.
    Type: Application
    Filed: October 16, 2019
    Publication date: May 13, 2021
    Applicant: Saudi Arabian Oil Company
    Inventors: Andrey Bakulin, Robert Smith, Stanislav Glubokovskikh