公开(公告)号：US20210333433A1

公开(公告)日：2021-10-28

申请号：US16325697

申请日：2017-09-01

Applicant: Landmark Graphics Corporation

Inventor： Jeffrey Marc Yarus ,  Rae Mohan Srivastava ,  Yevgeniy Zagayevskiy ,  Jin Fei ,  Yogendra Narayan Pandey

IPC: G01V99/00 ,  G01V1/40 ,  G06T11/00 ,  G06T3/40

Abstract: Systems and methods for modeling petroleum reservoir properties using a gridless reservoir simulation model are provided. Data relating to geological properties of a reservoir formation is analyzed. A tiered hierarchy of geological elements within the reservoir formation is generated at different geological scales, based on the analysis. The geological elements at each of the different geological scales in the tiered hierarchy are categorized. Spatial boundaries between the categorized geological elements are defined for each of the geological scales in the tiered hierarchy. A scalable and updateable gridless model of the reservoir formation is generated, based on the spatial boundaries defined for at least one of the geological scales in the tiered hierarchy.

公开(公告)号：US20200160173A1

公开(公告)日：2020-05-21

申请号：US16614858

申请日：2017-07-21

Applicant: Landmark Graphics Corporation

Inventor： Yogendra Narayan Pandey ,  Keshava Prasad Rangarajan ,  Jeffrey Marc Yarns ,  Naresh Chaudhary ,  Nagaraj Srinivasan ,  James Etienne

IPC: G06N3/08 ,  G06K9/62 ,  G01V99/00

Abstract: Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

公开(公告)号：US10388065B2

公开(公告)日：2019-08-20

申请号：US15762658

申请日：2015-11-10

Applicant: Landmark Graphics Corporation

Inventor： Jeffrey Marc Yarus ,  Rae Mohan Srivastava ,  Genbao Shi ,  Veronica Liceras ,  Yogendra Narayan Pandey ,  Zhaoyang Wang

IPC: G06T17/20 ,  G01V99/00 ,  G06F17/50 ,  E21B41/00 ,  E21B43/26 ,  G06T1/60

Abstract: Fracture networks are simulated using a large triangle mesh size for large fractures and a smaller triangle mesh size for small fractures. Input data defining parameters of one or more fractures are input, the fractures being comprised of a triangle mesh. A first triangle mesh size for the fractures is determined based upon the input data. A second smaller triangle mesh size is then determined based upon the input data. The fracture network is then simulated using the large and small triangle mesh sizes.

公开(公告)号：US20180276888A1

公开(公告)日：2018-09-27

申请号：US15762658

申请日：2015-11-10

Applicant: Landmark Graphics Corporation

Inventor： Jeffrey Marc Yarus ,  Rae Mohan Srivastava ,  Genbao Shi ,  Veronica Liceras ,  Yogendra Narayan Pandey ,  Zhaoyang Wang

IPC: G06T17/20

CPC classification number: G06T17/205 ,  E21B41/00 ,  E21B43/26 ,  G01V99/00 ,  G06F17/5009 ,  G06F17/5018 ,  G06T1/60 ,  G06T2215/16

Abstract: Fracture networks are simulated using a large triangle mesh size for large fractures and a smaller triangle mesh size for small fractures. Input data defining parameters of one or more fractures are input, the fractures being comprised of a triangle mesh. A first triangle mesh size for the fractures is determined based upon the input data. A second smaller triangle mesh size is then determined based upon the input data. The fracture network is then simulated using the large and small triangle mesh sizes.