Patents by Inventor Emmanuel Olumuyiwa Ibidunmoye

Emmanuel Olumuyiwa Ibidunmoye 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).

  • Machine learning-based reservoir reserves estimation
    Patent number: 11377932
    Abstract: A system, method, and computer program product for reservoir reserves estimation that includes at least one processing component, at least one memory component, a well data manager, a classification module, and an estimation module. The well data manager consolidates well data for a subject well and at least one neighbor well. The classification module includes a search component that selects at least one closest neighbor well from the neighbor wells. The classification module also includes a similarity matching component that determines a log signature distance between the at least one closest neighbor well and the subject well, as well as an analog selector that selects one or more analog wells from the at least one closest neighbor well. The estimation module includes a decline estimator that generates group decline rates for the one or more analog wells and a subject decline rate based on the group decline rates.
    Type: Grant
    Filed: November 19, 2020
    Date of Patent: July 5, 2022
    Assignee: International Business Machines Corporation
    Inventors: Sagar N. Purkayastha, Emmanuel Olumuyiwa Ibidunmoye, Moshood Omolade Saliu
  • MACHINE LEARNING-BASED RESERVOIR RESERVES ESTIMATION
    Publication number: 20220154556
    Abstract: A system, method, and computer program product for reservoir reserves estimation that includes at least one processing component, at least one memory component, a well data manager, a classification module, and an estimation module. The well data manager consolidates well data for a subject well and at least one neighbor well. The classification module includes a search component that selects at least one closest neighbor well from the neighbor wells. The classification module also includes a similarity matching component that determines a log signature distance between the at least one closest neighbor well and the subject well, as well as an analog selector that selects one or more analog wells from the at least one closest neighbor well. The estimation module includes a decline estimator that generates group decline rates for the one or more analog wells and a subject decline rate based on the group decline rates.
    Type: Application
    Filed: November 19, 2020
    Publication date: May 19, 2022
    Inventors: Sagar N. Purkayastha, Emmanuel Olumuyiwa Ibidunmoye, Moshood Omolade SALIU