公开(公告)号：US11846175B2

公开(公告)日：2023-12-19

申请号：US17136895

申请日：2020-12-29

Applicant: Landmark Graphics Corporation

Inventor： Soumi Chaki ,  Honggeun Jo ,  Terry Wong ,  Yevgeniy Zagayevskiy ,  Dominic Camilleri

IPC: E21B47/022 ,  E21B47/12 ,  G01V1/46 ,  G01V1/48 ,  G06N3/08

CPC classification number: E21B47/022 ,  E21B47/138 ,  G01V1/46 ,  G01V1/48 ,  G06N3/08 ,  E21B2200/20

Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.

公开(公告)号：US20220205354A1

公开(公告)日：2022-06-30

申请号：US17136895

申请日：2020-12-29

Applicant: Landmark Graphics Corporation

Inventor： Soumi Chaki ,  Honggeun Jo ,  Terry Wong ,  Yevgeniy Zagayevskiy ,  Dominic Camilleri

IPC: E21B47/022 ,  E21B47/12 ,  G06N3/08 ,  G01V1/46 ,  G01V1/48

Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.