US 12,360,272 B2
Cascaded machine-learning workflow for salt seismic interpretation
Anisha Kaul, Houston, TX (US); Cen Li, Missouri City, TX (US); Hiren Maniar, Houston, TX (US); and Aria Abubakar, Sugar Land, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 17/252,484
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
PCT Filed Jun. 26, 2019, PCT No. PCT/US2019/039160
§ 371(c)(1), (2) Date Dec. 15, 2020,
PCT Pub. No. WO2020/009850, PCT Pub. Date Jan. 9, 2020.
Claims priority of provisional application 62/694,404, filed on Jul. 5, 2018.
Prior Publication US 2021/0270983 A1, Sep. 2, 2021
Int. Cl. G06G 7/48 (2006.01); G01V 1/30 (2006.01); G01V 1/50 (2006.01); G06N 20/00 (2019.01)
CPC G01V 1/302 (2013.01) [G01V 1/50 (2013.01); G06N 20/00 (2019.01); G01V 2210/1234 (2013.01); G01V 2210/64 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
training a first machine learning model using data from a crossline direction and an inline direction of a seismic volume;
determining, using the first machine learning model, a top of salt (TOS) surface in the seismic volume based on crossline direction slices of the seismic volume and inline direction slices of the seismic volume;
determining a binary mask based upon the determined TOS surface;
initializing training data of the seismic volume based on output from the trained first machine learning model;
determining a depth in the seismic volume above which a predicted salt surface has a predetermined level of clarity;
sampling seismic data in the seismic volume in a depth direction to obtain a training seismic slice, wherein the training seismic slice includes information from the crossline direction, the inline direction, and the depth direction;
sampling the binary mask in the depth direction to obtain a mask slice;
selecting a first coordinate in the training seismic slice to produce a first tile;
selecting a second coordinate in the mask slice to produce a second tile;
updating the training data of the seismic volume based upon the first tile and the second tile;
training a second machine learning model using the updated training data;
determining, using the second machine learning model, a presence of a salt body in an evaluation seismic slice;
generating, by the second machine learning model, a three-dimensional (3D) model of a subterranean formation with the salt body delineated in the seismic volume;
displaying the 3D model of the subterranean formation with the salt body in the seismic volume;
determining, based on the 3D model of the subterranean formation, fluid flow characteristics of a reservoir in a field of the subterranean formation; and
controlling, based on the 3D model of the subterranean formation, drilling in the reservoir.