公开(公告)号：US20220128724A1

公开(公告)日：2022-04-28

申请号：US17510526

申请日：2021-10-26

Applicant: SHELL OIL COMPANY

Inventor： Satyakee SEN ,  Russell David Potter ,  Donald Paul Griffith ,  Sam Ahmad Zamanian ,  Sergey Frolov

IPC: G01V1/30 ,  G06N3/08

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

公开(公告)号：US11808906B2

公开(公告)日：2023-11-07

申请号：US17275302

申请日：2019-09-10

Applicant: SHELL OIL COMPANY

Inventor： Donald Paul Griffith ,  Sam Ahmad Zamanian ,  Russell David Potter

IPC: G01V1/30 ,  G06N3/084

CPC classification number: G01V1/306 ,  G01V1/302 ,  G01V1/308 ,  G06N3/084 ,  G01V2210/612 ,  G01V2210/614 ,  G01V2210/641 ,  G01V2210/642

Abstract: A method for training a backpropagation-enabled segmentation process is used for identifying an occurrence of a sub-surface feature. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A prediction of the occurrence of the subsurface feature has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.

公开(公告)号：US11525934B2

公开(公告)日：2022-12-13

申请号：US15931737

申请日：2020-05-14

Applicant: SHELL OIL COMPANY

Inventor： Donald Paul Griffith ,  Sam Ahmad Zamanian ,  Russell David Potter ,  Stéphane Youri Richard Michael Joachim Gesbert ,  Thomas Peter Merrifield

IPC: G01V1/30 ,  G06N3/08 ,  G01V99/00

Abstract: A method for a method for identifying a subsurface pore-filling fluid and/or lithology. A training set of field-acquired geophysical data and/or simulated geophysical data is provided to train a backpropagation-enabled process. The trained process is used on a field-acquired data set that is not part of the training set to infer presence of a subsurface pore-filling fluid and/or lithology.

公开(公告)号：US11802984B2

公开(公告)日：2023-10-31

申请号：US17510526

申请日：2021-10-26

Applicant: SHELL OIL COMPANY

Inventor： Satyakee Sen ,  Russell David Potter ,  Donald Paul Griffith ,  Sam Ahmad Zamanian ,  Sergey Frolov

IPC: G01V1/30 ,  G06N3/084 ,  G06F18/214 ,  G06N3/08 ,  G06N20/00 ,  G06N3/0895 ,  G06N3/09

CPC classification number: G01V1/30 ,  G06F18/2155 ,  G06N3/08 ,  G06N3/084 ,  G06N20/00 ,  G01V2210/6161 ,  G06N3/0895 ,  G06N3/09

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

公开(公告)号：US11698471B2

公开(公告)日：2023-07-11

申请号：US17275309

申请日：2019-09-10

Applicant: SHELL OIL COMPANY

Inventor： Donald Paul Griffith ,  Sam Ahmad Zamanian ,  Russell David Potter

IPC: G01V1/30 ,  G06N3/084

CPC classification number: G01V1/307 ,  G06N3/084 ,  G01V2210/63

Abstract: A method for training a backpropagation-enabled regression process is used for predicting values of an attribute of subsurface data. A multi-dimensional seismic data set with an input dimension of at least two is inputted into a backpropagation-enabled process. A predicted value of the attribute has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.