公开(公告)号：US20250076272A1

公开(公告)日：2025-03-06

申请号：US18457145

申请日：2023-08-28

Applicant: ARAMCO SERVICES COMPANY

Inventor： Tao Lin ,  Weichang Li ,  Mokhles M. Mezghani

IPC: G01N33/24 ,  E21B25/00 ,  G06T7/00

Abstract: A method for analyzing rock cores of a subterranean formation is disclosed. The method includes capturing low resolution core images of the rock cores, selecting, by a computer processor and based on a pre-determined quality threshold for qualifying the low resolution core images, a number of qualified rock cores, capturing high resolution core images of the qualified rock cores, generating, by the computer processor and based on a high resolution core image evaluation model, a ranking of the qualified rock cores, and analyzing, based at least on the ranking, the qualified rock cores to generate a core analysis result.

公开(公告)号：US11898901B2

公开(公告)日：2024-02-13

申请号：US17804511

申请日：2022-05-27

Applicant: ARAMCO SERVICES COMPANY

Inventor： Lei Fu ,  Weichang Li

IPC: G06N3/044 ,  G01H9/00 ,  G01V1/22 ,  G01V1/30 ,  G01V1/28

CPC classification number: G01H9/004 ,  G01V1/226 ,  G01V1/288 ,  G01V1/307 ,  G06N3/044

Abstract: A method for mapping fiber optic distributed acoustic sensing (DAS) measurements to particle motion involves obtaining, from a fiber optic DAS system in a wellbore, a first set of DAS data associated with a first seismic wave; obtaining, from a discrete seismic receiver in the wellbore, measured particle motion data associated with the first seismic wave; generating training data from the first set of DAS data and the measured particle motion data; training a machine learning model using the training data; obtaining a second set of DAS data associated with a second seismic wave; and determining a predicted particle motion in response to the second seismic wave using the machine learning model applied to the second set of DAS data.

公开(公告)号：US20230193751A1

公开(公告)日：2023-06-22

申请号：US17644845

申请日：2021-12-17

Applicant: ARAMCO SERVICES COMPANY

Inventor： Weichang Li ,  Murtadha J. AITammar ,  Khalid M. Alruwaili ,  Osman Hamid

IPC: E21B49/00 ,  G06N3/08 ,  E21B47/10

CPC classification number: E21B49/00 ,  G06N3/08 ,  E21B47/10 ,  E21B2200/20 ,  E21B2200/22

Abstract: A method may include obtaining well log data for various wells regarding a geological region of interest. The well log data may correspond to various well logs with different logging types. The method may include assigning, using a grouping algorithm, subsets of the well log data to various groups based on one or more geological attributes. The method may include determining, using the groups and a machine-learning algorithm, various well zones for different portions of a respective well among the wells. The method may include determining interpolated log data using the well log data, the well zones, and an intrawell interpolation process. The method may include generating a formation property volume based on the interpolated log data and the well log data.

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：US20250060499A1

公开(公告)日：2025-02-20

申请号：US18450174

申请日：2023-08-15

Applicant: ARAMCO SERVICES COMPANY

Inventor： Farhan Naseer ,  Ammar AlQatari ,  Weichang Li

IPC: G01V1/50 ,  E21B47/013 ,  E21B49/00 ,  G01V1/143 ,  G06N20/00

Abstract: Methods and systems for training a machine learning (ML) network to predict a likelihood of a presence of a geological fracture from an observed drill-bit seismic dataset are disclosed. The method may include obtaining, using a seismic processing system, a plurality of geophysical models, where each geophysical model includes a location of a drill bit. The method may further include simulating, for each geophysical model a corresponding simulated drill-bit seismic dataset for seismic waves emanating from the drill bit and recorded by at least one seismic receiver and forming a training dataset including a plurality of training pairs, with each training pair including a geophysical model from the plurality of geophysical models and the corresponding simulated drill-bit seismic dataset. The method may still further include training, using the training dataset, the ML network to predict the likelihood of the presence of the geological fracture from the observed drill-bit seismic dataset.

公开(公告)号：US12098632B2

公开(公告)日：2024-09-24

申请号：US17814449

申请日：2022-07-22

Applicant: ARAMCO SERVICES COMPANY

Inventor： Chicheng Xu ,  Mohamed Larbi Zeghlache ,  Tao Lin ,  Yuchen Jin ,  Weichang Li ,  Olanrewaju Abudu

IPC: E21B49/00

CPC classification number: E21B49/003

Abstract: A method to perform a field operation with well log repeatability verification is disclosed. The method includes generating, by repeatedly performing well logging of a wellbore penetrating a subterranean formation in a field, a set of well log data files each comprising a plurality of data channels, each data channel comprising a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, a main log and a repeat log of the set of well log data files to determine a repeatability measure of the set of well log data files, presenting, using a graphical user interface, the repeatability measure to a user, and facilitating, based on a user input in response to presenting the repeatability measure, the field operation.

公开(公告)号：US20230408327A1

公开(公告)日：2023-12-21

申请号：US17804511

申请日：2022-05-27

Applicant: ARAMCO SERVICES COMPANY

Inventor： Lei Fu ,  Weichang Li

IPC: G01H9/00 ,  G01V1/22 ,  G01V1/28 ,  G01V1/30 ,  G06N3/04

CPC classification number: G01H9/004 ,  G01V1/226 ,  G06N3/0445 ,  G01V1/307 ,  G01V1/288

Abstract: A method for mapping fiber optic distributed acoustic sensing (DAS) measurements to particle motion involves obtaining, from a fiber optic DAS system in a wellbore, a first set of DAS data associated with a first seismic wave; obtaining, from a discrete seismic receiver in the wellbore, measured particle motion data associated with the first seismic wave; generating training data from the first set of DAS data and the measured particle motion data; training a machine learning model using the training data; obtaining a second set of DAS data associated with a second seismic wave; and determining a predicted particle motion in response to the second seismic wave using the machine learning model applied to the second set of DAS data.