Patents by Inventor Honggeun Jo

Honggeun Jo 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).

  • Estimating reservoir production rates using machine learning models for wellbore operation control
    Patent number: 11846175
    Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.
    Type: Grant
    Filed: December 29, 2020
    Date of Patent: December 19, 2023
    Assignee: Landmark Graphics Corporation
    Inventors: Soumi Chaki, Honggeun Jo, Terry Wong, Yevgeniy Zagayevskiy, Dominic Camilleri
  • Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control
    Publication number: 20220205354
    Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.
    Type: Application
    Filed: December 29, 2020
    Publication date: June 30, 2022
    Inventors: Soumi Chaki, Honggeun Jo, Terry Wong, Yevgeniy Zagayevskiy, Dominic Camilleri