公开(公告)号：US20250076273A1

公开(公告)日：2025-03-06

申请号：US18459238

申请日：2023-08-31

Applicant: ARAMCO SERVICES COMPANY

Inventor： Ali Almadan ,  Weichang Li ,  Mustafa Ali H. Al Ibrahim

IPC: G01N33/24 ,  G06T5/00 ,  G06T5/40 ,  G06V10/50 ,  G06V10/762 ,  G06V10/764 ,  G06V20/70

Abstract: A method may include obtaining a petrographic image. The method may further include determining various region proposals based on the petrographic image and a selective searching function. A respective region proposal among the region proposals may correspond to various pixels in the petrographic image according to a predetermined dimension. The method may further include determining color histogram data for the petrographic image. The method may further include determining input image data based on the petrographic image, the region proposals, and the color histogram data. The method may further include determining a rock object using the input image data and a machine-learning model.

公开(公告)号：US20250078472A1

公开(公告)日：2025-03-06

申请号：US18458005

申请日：2023-08-29

Applicant: ARAMCO SERVICES COMPANY

Inventor： Weichang Li ,  Osama B. Dabbousi ,  Saif S. Alhajri ,  Mustafa Ali H. Al Ibrahim

IPC: G06V10/774 ,  G06F16/951 ,  G06T7/40 ,  G06V10/764 ,  G06V20/70

Abstract: A method and a system for generating a labeled benchmark dataset are disclosed. The method includes obtaining a plurality of sources related to a thin section using web scraping and extracting a plurality of images from the plurality of sources related to the thin section, the plurality of images including a plurality of thin section images and a plurality of non-thin section images. Further, the method includes determining the plurality of thin section images from the plurality of extracted images and generating a classification of the plurality of thin section images based on a given classification criteria. The geological thin-section based machine learning models is trained based on the generated classification of the plurality of thin section images and a wellbore drilling plan is generated based on the geological thin-section based machine learning models.