公开(公告)号：US20240402383A1

公开(公告)日：2024-12-05

申请号：US18325277

申请日：2023-05-30

Applicant: Chevron U.S.A. Inc.

Inventor： Yula Tang ,  Yuanbo Lin ,  Suk Kyoon Choi ,  Jianlei Sun

IPC: G01V99/00 ,  E21B47/06

Abstract: A hybrid modeling approach incorporates both physics-based reservoir modeling and machine learning technique to capture dynamic behavior of unconventional wells. Shut-in bottom hole pressure for unconventional wells are simulated for use as proxy for reservoir pressure in unconventional reservoirs. Production parameters for unconventional wells (e.g., gas/oil ratio, water cut, flowing bottom hole pressure, shut-in bottom hole pressure, productivity index) are determined for use in controlling the operations of unconventional wells.

公开(公告)号：US20230272703A1

公开(公告)日：2023-08-31

申请号：US17681109

申请日：2022-02-25

Applicant: Chevron U.S.A. Inc.

Inventor： Suk Kyoon Choi ,  Daegil Yang ,  Chunyan Xie ,  Dongjae Kam

IPC: E21B44/00 ,  E21B47/06

CPC classification number: E21B44/00 ,  E21B47/06 ,  E21B2200/20 ,  E21B2200/22

Abstract: A machine learning model is trained to facilitate determination of transient inflow performance relationship for a reservoir. A type reservoir model for the reservoir is developed and run multiple times with different input parameters to generate multiple production simulations for the reservoir. The input parameters and the results of the multiple production simulations for the reservoir are used to train a machine learning model. The trained machine learning model facilitates determination of transient inflow performance relationship for the reservoir by providing time-series prediction of average pressure, production rate, and absolute open flow of the reservoir.