公开(公告)号：US20240119106A1

公开(公告)日：2024-04-11

申请号：US17963559

申请日：2022-10-11

Applicant: Chevron U.S.A. Inc.

Inventor： Mason C. Edwards ,  Kevin Kaczorowski ,  Paymon Hossaini

IPC: G06F17/11 ,  G06F7/50

CPC classification number: G06F17/11 ,  G06F7/50

Abstract: Application logs to be normalized may be grouped into (1) mutable logs to be changed through normalization, and (2) context logs that remain constant. Reference logs may be grouped into same types of logs. Multivariate linear transformation may be performed on the application logs using the reference logs, with the parameters of the multivariate linear transformation adjusted based on comparison of the probability distribution of reference logs with the probability distribution of normalized application logs.

公开(公告)号：US20230400598A1

公开(公告)日：2023-12-14

申请号：US17826400

申请日：2022-05-27

Applicant: Chevron U.S.A. Inc.

Inventor： Mason C. Edwards ,  Joshua S. Hoskinson ,  Sandra C. Saldana ,  Donald Neal Burch

IPC: G01V1/50 ,  G06N20/00

CPC classification number: G01V1/50 ,  G01V2210/60 ,  G06N20/00

Abstract: A reference curve may be used as the goal for alignment when depth shifting one or more target well logs. Traditionally the reference curve has been measured data, and is usually of the same measurement type as the well log for shifting when performed algorithmically. The reference curve may be generated by a weak learner machine learning model. The weak learner machine learning model may preserve shape characteristics and depth information of one or more input curves in the reference curve. Depth shifting of a target well log may be performed by iteratively using sliding correlation windows of differing sizes.

公开(公告)号：US20230134372A1

公开(公告)日：2023-05-04

申请号：US17514937

申请日：2021-10-29

Applicant: Chevron U.S.A. Inc.

Inventor： Mason C. Edwards ,  Evan James Earnest-Heckler ,  Stephen D. Yow ,  Min Li

IPC: G06K9/46 ,  G06T7/10 ,  G06T7/70

Abstract: An image log of a subsurface region may be divided into multiple image log segments. The multiple image log segments may be processed through a computer vision neural network to identify both (1) the types of subsurface features within the subsurface region, and (2) the locations of the subsurface features within the subsurface region.