US 12,416,233 B2
Enhanced measurement-while-drilling decoding using artificial intelligence
Kenneth Miller, Houston, TX (US); David Erdos, Houston, TX (US); and Abraham Erdos, Houston, TX (US)
Assigned to ERDOS MILLER, INC., Houston, TX (US)
Appl. No. 18/021,917
Filed by ERDOS MILLER, INC., Houston, TX (US)
PCT Filed Aug. 19, 2021, PCT No. PCT/US2021/046633
§ 371(c)(1), (2) Date Feb. 17, 2023,
PCT Pub. No. WO2022/040393, PCT Pub. Date Feb. 24, 2022.
Claims priority of provisional application 63/068,176, filed on Aug. 20, 2020.
Prior Publication US 2023/0332498 A1, Oct. 19, 2023
Int. Cl. E21B 47/18 (2012.01)
CPC E21B 47/18 (2013.01) [E21B 2200/22 (2020.05)] 19 Claims
OG exemplary drawing
 
18. A system comprising:
one or more memory devices storing instructions; and
one or more processing devices communicatively coupled to the one or more memory devices and configured to execute the instructions to:
receive a mud pulse signal from a measurement while drilling (MWD) tool, wherein the mud pulse signal comprises data;
decode, using a trained machine learning model, the data to determine a value of the data;
provide a user interface comprising the value of the data for presentation on a computing device of a user;
provide a second user interface for presentation on the computing device of the user, wherein the second user interface presents a graphical element for identifying where a synchronization signal is located in the mud pulse signal;
receive, from the computing device, a message identifying where the synchronization signal is located in the mud pulse signal; and
update, using the message, the trained machine learning model to classify the data in the mud pulse signal by identifying the synchronization signal, such that the trained machine learning model identifies subsequent synchronization signals in subsequent mud pulse signals based on where the synchronization signal is located in the mud pulse signal.