US 12,252,979 B2
Event detection from pump data
Crispin Chatar, Katy, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Filed by Schlumberger Technology Corporation, Sugar Land, TX (US)
Filed on Mar. 19, 2024, as Appl. No. 18/609,441.
Application 18/609,441 is a continuation of application No. 17/594,389, granted, now 12,018,560, previously published as PCT/US2019/028045, filed on Apr. 18, 2019.
Prior Publication US 2024/0218784 A1, Jul. 4, 2024
Int. Cl. E21B 47/06 (2012.01); G01V 1/50 (2006.01)
CPC E21B 47/06 (2013.01) [G01V 1/50 (2013.01); E21B 2200/20 (2020.05); G01V 2210/663 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for detecting a downhole event within a wellbore, comprising:
receiving historical pressure data collected with one or more pressure sensors at a plurality of wellbores, the historical pressure data comprising a plurality of historical pressure pulses within drilling fluid at the plurality of wellbores, the plurality of historical pressure pulses being associated with a plurality of downhole operations of a plurality of downhole tools within the plurality of wellbores;
generating a plurality of historical pressure spectrograms from the historical pressure data, the plurality of historical pressure spectrograms each representing a spectrum of frequencies of the plurality of historical pressure pulses over time;
identifying, independent of the plurality of historical pressure spectrograms, a plurality of historical downhole events that occurred during the plurality of downhole operations;
correlating the plurality of historical downhole events to the plurality of historical pressure spectrograms to generate labelled training pressure spectrograms;
training a pressure spectrogram machine learning model based on the labelled training pressure spectrograms to identify pressure signatures in the labelled training pressure spectrograms corresponding to the identified plurality of historical downhole events, the pressure spectrogram machine learning model being trained to identify downhole events from input pressure spectrograms;
identifying, with a pressure sensor at a surface of a target wellbore, target pressure data comprising a plurality of target pressure pulses within a flow of a target drilling fluid, the plurality of target pressure pulses being associated with a target downhole operation of a target downhole tool;
based on the target pressure data, generating a target pressure spectrogram;
using the target pressure spectrogram as input to the pressure spectrogram machine learning model, identifying a target downhole event affecting the target downhole operation; and
based on identifying the target downhole event, generating a notification indicating an abnormal operation of the target downhole tool.