SPECTRAL ANALYSIS AND MACHINE LEARNING OF ACOUSTIC SIGNATURE OF WIRELINE STICKING
This disclosure describes systems, methods, and apparatuses for preventing wireline sticking during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations measured in fracking fluid in the wellhead, fluid line, or standpipe into an electrical signal in a time domain; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the time domain electrical signal into a frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum as associated with increasing wireline friction, the machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system; and a user interface configured to return an indication of the increasing wireline friction to an operator of the hydraulic fracturing operations.
1 . A system for preventing wireline sticking during hydraulic fracturing operations, the system comprising:
a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations measured in fracking fluid in the fracking wellhead, circulating fluid line, or standpipe into an electrical signal in a time domain;
a memory configured to store the electrical signal;
a converter configured to access the electrical signal from the memory and convert the electrical signal in a window of time into a current frequency domain spectrum;
a machine-learning system configured to classify the current frequency domain spectrum as associated with increasing wireline friction, the machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system; and
a user interface configured to return an indication of the increasing wireline friction to an operator of the hydraulic fracturing operations.
2 . The system of claim 1 , wherein the acoustic vibrations are caused by the wireline rubbing against walls of a borehole.
3 . (canceled)
4 . The system of claim 1 , wherein the sensor is an acoustic sensor.
5 . (canceled)
6 . The system of claim 1 , wherein the machine-learning system considers a width of one or more frequency peaks in the current frequency domain spectrum.
7 . The system of claim 6 , wherein the machine-learning system considers a number of frequency peaks in the current frequency domain spectrum.
8 . The system of claim 1 , wherein the machine-learning system considers a number of frequency peaks in the current frequency domain spectrum.
9 . The system of claim 8 , wherein the machine-learning system considers an amplitude of the one or more frequency peaks in the current frequency domain.
10 . The system of claim 1 , wherein the machine-learning system is trained on previous frequency domain spectra as a machine-learning input and associated wireline sticking events as a machine-learning output.
11 . (canceled)
12 . The system of claim 1 , wherein the machine-learning system is trained to classify the current frequency domain spectrum on a grouping of previous frequency domain spectra measured during previous wireline operations that most closely matches the current frequency domain spectra.
13 . The system of claim 1 , wherein the machine-learning system is trained on wireline sticking events as a machine-learning input and associated previous frequency domain spectra as a machine-learning output.
14 . The system of claim 1 , wherein the machine-learning system is configured to classify based on a grouping of frequency domain spectra measured during previous wireline operations that most closely match the current frequency domain spectra.
15 . (canceled)
16 . The system of claim 1 , wherein the machine-learning system is further configured to analyze the electrical signal for the window of time in the time domain in conjunction with analyzing the current frequency domain spectrum to classify the current frequency domain spectrum.
17 . (canceled)
18 . The system of claim 1 , wherein the machine-learning system is configured to classify the current frequency domain spectrum as associated with a start or end of plug transport down the well.
19 . (canceled)
20 . (canceled)
21 . The system of claim 1 , wherein wireline friction is identified by an increase in a number or width of frequency peaks in the current frequency domain spectrum.
22 . (canceled)
23 . The system of claim 1 , further comprising:
a wellbore with a casing; and
a fracking pump.
24 . A method of preventing wireline sticking during hydraulic fracturing operations, the method comprising:
providing a sensor coupled to a wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations in fracking fluid in the wellhead, circulating fluid line, or standpipe into an electrical signal in a time domain;
recording the electrical signal to a memory;
converting the electrical signal in the memory for a window of time to a current frequency domain spectrum comprising an amplitude spike at one or more frequencies;
analyzing the current frequency domain spectrum via a machine-learning system trained on previous frequency domain spectra measured during previous wireline operations and previously classified by the machine-learning system;
classifying the current frequency domain spectrum as associated with increased wireline friction; and
returning an indication of the increasing wireline friction to a well operator.
25 . The method of claim 24 , wherein the acoustic vibrations are caused by the wireline rubbing against walls of a borehole.
26 . (canceled)
27 . The method of claim 24 , wherein the sensor is an acoustic sensor.
28 . (canceled)
29 . The method of claim 24 , wherein the analyzing considers a width of one or more frequency peaks in the current frequency domain spectrum.
30 . The method of claim 29 , wherein the analyzing considers a number of frequency peaks in the current frequency domain spectrum.
31 . The method of claim 24 , wherein the analyzing considers a number of frequency peaks in the current frequency domain spectrum.
32 . The method of claim 31 , wherein the analyzing considers an amplitude of the one or more frequency peaks in the current frequency domain.
33 . The method of claim 24 , wherein the machine-learning system is trained on previous frequency domain spectra as a machine-learning input and associated wireline sticking events as a machine-learning output.
34 . (canceled)
35 . The method of claim 24 , wherein the classifying is based on a grouping of frequency domain spectra measured during previous wireline operations that most closely matches the current frequency domain spectra.
36 . (canceled)
37 . The method of claim 24 , wherein the machine-learning system is trained on wireline sticking events as a machine-learning input and associated previous frequency domain spectra as a machine-learning output.
38 . (canceled)
39 . (canceled)
40 . (canceled)
41 . The method of claim 24 , further comprising: classifying the current frequency domain spectrum as associated with a start or end of plug transport down the well.
42 . (canceled)
43 . (canceled)
44 . (canceled)
45 . The method of claim 24 , wherein the classifying is based on the acoustic vibrations increasing a threshold level above an average of acoustic vibrations during wireline descent.
46 . The method of claim 24 , wherein increased wireline friction is identified by an increase in a number or width of frequency peaks in the current frequency domain spectrum.
47 . A method of preventing wireline sticking, the method comprising:
starting a wireline operation on a fracking stage of a well;
measuring acoustic vibrations in fracking fluid in a wellhead, circulating fluid line, or standpipe of the well;
converting the acoustic vibrations into an electrical signal in a time domain;
recording the electrical signal to a memory;
converting the electrical signal in the memory for a window of time to a current frequency domain spectrum comprising an amplitude spike at one or more frequencies;
analyzing the current frequency domain spectrum via a machine-learning system trained on previous frequency domain spectra measured during previous wireline stages and previously classified by the machine-learning system; and
classifying the current frequency domain spectrum as associated with increased wireline friction; and
adjusting a parameter of the wireline operation based on the increased wireline friction.
48 . The method of claim 47 , wherein the acoustic vibrations are caused by the wireline rubbing against walls of a borehole.
49 . (canceled)
50 . The method of claim 47 , wherein the sensor is an acoustic sensor.
51 . (canceled)
52 . The method of claim 47 , wherein the analyzing considers a width of one or more frequency peaks in the current frequency domain spectrum.
53 . The method of claim 52 , wherein the analyzing considers a number of frequency peaks in the current frequency domain spectrum.
54 . The method of claim 47 , wherein the analyzing considers a number of frequency peaks in the current frequency domain spectrum.
55 . The method of claim 54 , wherein the analyzing considers an amplitude of the one or more frequency peaks in the current frequency domain.
56 . The method of claim 47 , wherein the machine-learning system is trained on previous frequency domain spectra as a machine-learning input and associated wireline sticking events as a machine-learning output.
57 . (canceled)
58 . The method of claim 47 , wherein the classifying is based on a grouping of frequency domain spectra measured during previous wireline operations that most closely matches the current frequency domain spectra.
59 . (canceled)
60 . The method of claim 47 , wherein the machine-learning system is trained on wireline sticking events as a machine-learning input and associated previous frequency domain spectra as a machine-learning output.
61 . The method of claim 47 , wherein the classifying is based on a grouping of frequency domain spectra measured during previous wireline operations that most closely match the current frequency domain spectra.
62 . The method of claim 47 , further comprising analyzing the electrical signal for the window of time in the time domain and using this in addition to the analyzing the current frequency domain spectrum to perform the classifying.
63 . (canceled)
64 . The method of claim 47 , further comprising classifying the current frequency domain spectrum as associated with a start or end of plug transport down the well.
65 . (canceled)
66 . (canceled)
67 . (canceled)
68 . The method of claim 47 , wherein the classifying is based on the acoustic vibrations increasing a threshold level above an average of acoustic vibrations during wireline descent.
69 . The method of claim 47 , wherein increased wireline friction is identified by an increase in a number or width of frequency peaks in the current frequency domain spectrum.
70 . The method of claim 47 , wherein the parameter of the wireline operation is perforation gun pressure, fracking stage duration, a pressure of fluid forced into a subterranean formation, pH of the fracking fluid pumped into the well, or a length of flush time.
71 . (canceled)
72 . (canceled)
73 . (canceled)
74 . (canceled)