US 12,326,532 B2
Feature detection in seismic data
Sunil Manikani, Pune (IN); Karan Pathak, Delhi (IN); Gayatri Novenita, Pune (IN); Hiren Maniar, Houston, TX (US); and Aria Abubakar, Houston, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 17/768,560
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Oct. 7, 2020, PCT No. PCT/US2020/070626
§ 371(c)(1), (2) Date Apr. 13, 2022,
PCT Pub. No. WO2021/077127, PCT Pub. Date Apr. 22, 2021.
Claims priority of provisional application 62/914,608, filed on Oct. 14, 2019.
Prior Publication US 2023/0341577 A1, Oct. 26, 2023
Int. Cl. G01V 1/34 (2006.01); G06T 7/11 (2017.01); G06T 7/73 (2017.01)
CPC G01V 1/345 (2013.01) [G06T 7/11 (2017.01); G06T 7/73 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20101 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving seismic training data comprising a plurality of images each including a plurality of traces;
predicting a geographic location of a feature in at least some of the plurality of traces based on a graphical location of a maximum of an amplitude peak therein, wherein the geographic location identifies the feature as comprising a sea floor or not comprising a sea floor;
applying a respective geographic label to each geographic location, wherein the geographic labels identify the geographic locations as comprising the sea floor or not comprising the sea floor;
classifying pixels of the plurality of images as representing the feature or not representing the feature, using a semantic segmentation model;
adjusting the geographic labels based on the classification of the pixels;
training, using the adjusted geographic labels and the seismic training data, a machine-learning model to identify the feature;
identifying the feature in a different seismic data set using the trained machine-learning model; and
modifying a drilling plan based on the identified features as determined by the trained machine-learning model.