Seismic Data Processing Artificial Intelligence

Patent metadata

Field	Value
Publication number	US-2019391295-A1
Application number	US-201616334744-A
Country	US
Kind code	A1
Filing date	Nov 7, 2016
Priority date	Nov 7, 2016
Publication date	Dec 26, 2019
Grant date	—

Abstract

Systems, methods, and non-transitory computer-readable media for processing geological data, of which the method includes receiving a geological data processing tool at a client system. The geological data processing tool includes artificial intelligence, and the geological data processing tool is generated by a geological processing tool provider. The method also includes obtaining training data for the geological data processing tool. The training data includes a plurality of labels. The method also includes training the geological data processing tool based on the training data, receiving data representing a physical, subterranean volume, identifying one or more geological features in the subterranean volume by using the geological data processing tool after training the geological data processing tool, and modifying, using the client system, one or more parameters of the geological data processing tool, or one or more labels of the plurality of labels.

First claim

1 . A method for processing geological data, the method comprising:
receiving a geological data processing tool at a client system, wherein the geological data processing tool comprises artificial intelligence, and wherein the geological data processing tool is generated by a geological processing tool provider; obtaining training data for the geological data processing tool, wherein the training data comprises a plurality of labels; training the geological data processing tool based on the training data; receiving data representing a physical, subterranean volume; identifying one or more geological features in the subterranean volume by using the geological data processing tool after training the geological data processing tool; and modifying, using the client system, one or more parameters of the geological data processing tool, or one or more labels of the plurality of labels.

2 . The method of claim 1 , wherein the training data is protected from exposure to a provider system from which the geological processing tool is received.

3 . The method of claim 2 , wherein the client system comprises a remote server accessible over the internet, and wherein the training data is held in an encrypted database accessible by the remote server and not accessible to the provider system.

4 . The method of claim 1 , wherein the artificial intelligence comprises a convolutional neural network, a support vector machine, or both.

5 . The method of claim 1 , wherein training the geological data processing tool comprises receiving an identification of one or more geological features in one or more images of the training data, wherein the plurality of labels are extensible by the client system.

6 . The method of claim 1 , wherein the one or more geological features comprise at least one of horizons, layer interfaces, geobodies, faults, sand channels, gas chimneys, pipelines, boreholes, well top markers, or pore pressure.

7 . The method of claim 1 , wherein receiving the training data comprises obtaining the training data from a database accessible to the geological tool provider.

8 . The method of claim 1 , wherein receiving the training data comprises receiving the training data from the client system, and wherein the geological processing tool is not accessible to the geological processing tool provider after being trained.

9 . A computing system, comprising:
one or more processors; and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
receiving a geological data processing tool at a client system, wherein the geological data processing tool comprises artificial intelligence, and wherein the geological data processing tool is generated by a geological processing tool provider;
obtaining training data for the geological data processing tool, wherein the training data comprises a plurality of labels;
training the geological data processing tool based on the training data;
receiving data representing a physical, subterranean volume;
identifying one or more geological features in the subterranean volume by using the geological data processing tool after training the geological data processing tool; and
modifying, using the client system, one or more parameters of the geological data processing tool, or one or more labels of the plurality of labels.

10 . The system of claim 9 , wherein the training data is protected from exposure to a provider system from which the geological processing tool is received.

11 . The system of claim 10 , wherein the client system comprises a remote server accessible over the internet, and wherein the training data is held in an encrypted database accessible by the remote server and not accessible to the provider system.

12 . The system of claim 9 , wherein the artificial intelligence comprises a convolutional neural network, a support vector machine, or both.

13 . The system of claim 9 , wherein training the geological data processing tool comprises receiving an identification of one or more geological features in one or more images of the training data, wherein the plurality of labels are extensible by the client system.

14 . The system of claim 9 , wherein the one or more geological features comprise at least one of horizons, layer interfaces, geobodies, faults, sand channels, gas chimneys, pipelines, boreholes, well top markers, or pore pressure.

15 . The system of claim 9 , wherein receiving the training data comprises obtaining the training data from a database accessible to the geological tool provider.

16 . The system of claim 9 , wherein receiving the training data comprises receiving the training data from the client system, and wherein the geological processing tool is not accessible to the geological processing tool provider after being trained.

17 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:
receiving a geological data processing tool at a client system, wherein the geological data processing tool comprises artificial intelligence, and wherein the geological data processing tool is generated by a geological processing tool provider; obtaining training data for the geological data processing tool, wherein the training data comprises a plurality of labels; training the geological data processing tool based on the training data; receiving data representing a physical, subterranean volume; identifying one or more geological features in the subterranean volume by using the geological data processing tool after training the geological data processing tool; and modifying, using the client system, one or more parameters of the geological data processing tool, or one or more labels of the plurality of labels.

18 . The medium of claim 17 , wherein the training data is protected from exposure to a provider system from which the geological processing tool is received.

19 . The medium of claim 18 , wherein the client system comprises a remote server accessible over the internet, and wherein the training data is held in an encrypted database accessible by the remote server and not accessible to the provider system.

20 . The medium of claim 17 , wherein the artificial intelligence comprises a convolutional neural network, a support vector machine, or both.

Assignees

• Schlumberger Technology Corp

Inventors

• Salman Nader
• Aarre Victor
• Tronerud Jarl Eirik

Classifications

• G01V99/005 Primary

  Physics · mapped topic

• G01V20/00 Primary

  Geomodelling in general · CPC title