US 11,852,769 B2
Utilization of geologic orientation data
Alfred Lacazette, Lakewood, CO (US)
Assigned to Geothermal Technologies, Inc., Bel Air, MD (US)
Filed by Geothermal Technologies, Inc., Bel Air, MD (US)
Filed on May 17, 2021, as Appl. No. 17/322,583.
Prior Publication US 2022/0365236 A1, Nov. 17, 2022
Int. Cl. G01V 1/34 (2006.01); G06T 11/00 (2006.01); G01V 1/30 (2006.01)
CPC G01V 1/345 (2013.01) [G01V 1/301 (2013.01); G01V 1/306 (2013.01); G06T 11/003 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by one or more computing devices, a discrete three-dimensional representation of a geologic volume comprising a set of three-dimensional orientations in the geologic volume, wherein:
each three-dimensional orientation in the set of three-dimensional orientations is represented as a set of direction-angles measured relative to a set of coordinate axes,
each direction-angle in the set of direction-angles is a scalar value, and
each direction-angle in the set of direction-angles is assigned to a respective channel in a set of channels;
receiving, by the one or more computing devices, a set of other measurements of properties of the geologic volume;
correlating, by the one or more computing devices, the set of three-dimensional orientations with the set of other measurements to generate a geologic correlation data structure;
identifying, by the one or more computing devices, a geologic feature or a geologic attribute associated with the geologic volume based on the geologic correlation data structure;
training a direction-angle classifier machine learning model comprising a process of:
generating, for each direction-angle in the set of direction-angles, a respective probability value that the respective direction-angle corresponds to a property of a hot sedimentary aquifer (HSA),
modifying, based on the respective probability value generated for the respective direction-angle, each direction-angle in the set of direction-angles to generate a set of modified direction-angles, and
generating a set of classified direction-angles based on the set of modified direction-angles;
classifying, by the one or more computing devices, each direction-angle in the set of direction-angles as a classified direction-angle in the set of classified direction-angles using the direction-angle classifier machine learning model;
generating, by the one or more computing devices, a set of classified three-dimensional orientations based on the set of classified direction-angles;
correlating, by the one or more computing devices, the set of classified three-dimensional orientations with the set of other measurements to generate a classified geologic correlation data structure; and
identifying, by the one or more computing devices, the geologic feature or the geologic attribute associated with the geologic volume based on the classified geologic correlation data structure.