USING DEEP-LEARNING MODELS TO AUTOMATICALLY IDENTIFY SUBSURFACE RESERVOIR BOUNDARIES IN REAL TIME

Number of patents in Portfolio can not be more than 2000

United States of America

APP PUB NO 20250116176A1
SERIAL NO

18480650

Stats

ATTORNEY / AGENT: (SPONSORED)

Importance

Loading Importance Indicators... loading....

Abstract

See full text

The disclosure focuses on using a boundary identification system to actively determine borders and boundaries in subsurface geological features, such as reservoirs. In various implementations, the boundary identification system uses an ensemble image model leveraging multiple image-to-image machine-learning models to efficiently and accurately generate reservoir boundaries from inversion result profiles and images. In many instances, the boundary identification system generates reservoir boundaries from inversion results in real-time. Additionally, in some instances, the boundary identification system further improves the accuracy of the ensemble image model by diversifying the inputs and using ensembling on the individual model outputs during inference.

Loading the Abstract Image... loading....

First Claim

See full text

Family

Loading Family data... loading....

Patent Owner(s)

Patent OwnerAddress
SCHLUMBERGER TECHNOLOGY CORPORATION300 SCHLUMBERGER DRIVE SUGAR LAND TX 77478

International Classification(s)

  • [Classification Symbol]
  • [Patents Count]

Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Leveque, Soazig Seria, BN 4 21
Li, Ji Beijing, CN 263 1125
Li, Zhenhua Singapore, SG 114 493
Liu, Bingqi Beijing, CN 2 0
Toghi, Farid Beijing, CN 6 9
Wang, Fei Tianjin, CN 1116 10607

Cited Art Landscape

Load Citation

Patent Citation Ranking

Forward Cite Landscape

Load Citation