US 12,338,723 B2
System and method for measuring characteristics of cuttings from drilling operations with computer vision
Peter A. Torrione, Durham, NC (US)
Assigned to HELMERICH & PAYNE TECHNOLOGIES, LLC, Tulsa, OK (US)
Filed by HELMERICH & PAYNE TECHNOLOGIES, LLC, Tulsa, OK (US)
Filed on Jun. 26, 2024, as Appl. No. 18/754,956.
Application 18/754,956 is a continuation of application No. 17/808,986, filed on Jun. 25, 2022, granted, now 12,049,812.
Application 17/808,986 is a continuation of application No. 16/749,588, filed on Jan. 22, 2020, granted, now 11,408,266, issued on Aug. 9, 2022.
Application 16/749,588 is a continuation of application No. 14/938,962, filed on Nov. 12, 2015, granted, now 10,577,912, issued on Mar. 3, 2020.
Claims priority of provisional application 62/212,233, filed on Aug. 31, 2015.
Claims priority of provisional application 62/212,252, filed on Aug. 31, 2015.
Claims priority of provisional application 62/078,573, filed on Nov. 12, 2014.
Prior Publication US 2024/0344442 A1, Oct. 17, 2024
Int. Cl. E21B 44/00 (2006.01); E21B 21/06 (2006.01); E21B 49/00 (2006.01); G01N 15/0227 (2024.01); G01N 33/24 (2006.01); G05B 15/02 (2006.01)
CPC E21B 44/00 (2013.01) [E21B 21/065 (2013.01); E21B 49/005 (2013.01); G01N 15/0227 (2013.01); G01N 33/24 (2013.01); G05B 15/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer vision system for monitoring cuttings from drilling, the computer vision system comprising:
a camera oriented to face at least a portion of a screen surface of a shaker having drilling mud or cuttings thereon from a well during drilling;
a processor coupled to the camera; and
a memory coupled to the processor, wherein the memory comprises instructions executable by the processor to:
acquire a first plurality of images from the camera;
detect a first plurality of particles from the first plurality of images;
obtain first particle data associated with the first plurality of images;
model a first distribution of the first plurality of particles based at least in part on the first particle data associated with the first plurality of images;
acquire a second plurality of images from the camera;
detect a second plurality of particles from the second plurality of images;
obtain second particle data associated with the second plurality of images;
model a second distribution of the second plurality of particles based at least in part on the second particle data associated with the second plurality of images;
determine a difference in a likelihood of a distribution of particles based at least in part on the first distribution and the second distribution; and
provide an alert if the difference in the likelihood of the distribution of particles falls outside a threshold range.