US 11,725,505 B2
Machine learning mud pulse recognition networks
Dingding Chen, Tomball, TX (US); Li Gao, Katy, TX (US); Joni Polili Lie, Spring, TX (US); Paravastu Badrinarayanan, Houston, TX (US); Faisal Farooq Shah, Houston, TX (US); Bipin K. Pillai, San Jose, CA (US); and Murali Krishna Thottempudi, Katy, TX (US)
Assigned to Halliburton Energy Services, Inc., Houston, TX (US)
Filed by Halliburton Energy Services, Inc., Houston, TX (US)
Filed on Feb. 7, 2022, as Appl. No. 17/665,999.
Application 17/665,999 is a continuation of application No. 17/131,204, filed on Dec. 22, 2020, granted, now 11,255,187.
Prior Publication US 2022/0195868 A1, Jun. 23, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. E21B 47/18 (2012.01); G06N 20/00 (2019.01)
CPC E21B 47/18 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining a mud pulse data transmission from a mud pulser located in a borehole;
processing the mud pulse data transmission to generate normalized corrected data; and
generating SNR corrected data from the mud pulse data transmission utilizing a machine learning mud pulse recognition network (MPRN) and the normalized corrected data as an input to the machine learning MPRN.