Patents by Inventor Jacquelyn Daves

Jacquelyn Daves 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).

  • Detection of hydrocarbon presence in subsurface from seismic images using relational learning
    Patent number: 12468057
    Abstract: A computer-implemented method for detecting geological elements or fluid in a subsurface from seismic images is disclosed. Seismic data may be analyzed to identify one or both of fluid or geologic elements in the subsurface. As one example, the analysis may include unsupervised learning, such as variational machine learning, in order to learn relationships between different sets of seismic data. For example, variational machine learning may be used to learn relationships among partially-stack images or among pre-stack images in order to detect hydrocarbon presence. In this way, an unsupervised learning framework may be used for learning a Direct Hydrocarbon Indicator (DHI) from seismic images by learning relationships among partially-stack or pre-stack images.
    Type: Grant
    Filed: September 13, 2021
    Date of Patent: November 11, 2025
    Assignee: ExxonMobil Technology and Engineering Company
    Inventors: Kuang-Hung Liu, Huseyin Denli, Mary Johns, Jacquelyn Daves
  • DETECTION OF HYDROCARBON PRESENCE IN SUBSURFACE FROM SEISMIC IMAGES USING RELATIONAL LEARNING
    Publication number: 20230375735
    Abstract: A computer-implemented method for detecting geological elements or fluid in a subsurface from seismic images is disclosed. Seismic data may be analyzed to identify one or both of fluid or geologic elements in the subsurface. As one example, the analysis may include unsupervised learning, such as variational machine learning, in order to learn relationships between different sets of seismic data. For example, variational machine learning may be used to learn relationships among partially-stack images or among pre-stack images in order to detect hydrocarbon presence. In this way, an unsupervised learning framework may be used for learning a Direct Hydrocarbon Indicator (DHI) from seismic images by learning relationships among partially-stack or pre-stack images.
    Type: Application
    Filed: September 13, 2021
    Publication date: November 23, 2023
    Applicant: ExxonMobil Engineering and Technology Company
    Inventors: Kuang-Hung Liu, Huseyin Denli, Mary Johns, Jacquelyn Daves