Event characterization using hybrid das/dts measurements

Abstract

A method of determining the presence and/or extent of an event comprises determining a plurality of temperature features from a temperature sensing signal, determining one or more frequency domain features from an acoustic signal, and using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine the presence and/or extent of the event at one or more locations.

1 . A method of determining a presence or extent of an event, the method comprising:
 determining a plurality of temperature features from a temperature sensing signal;   determining one or more frequency domain features from an acoustic signal; and   using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine a presence or extent of the event at one or more locations.   
     
     
         2 . The method of  claim 1 , wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: a fluid inflow, a fluid outflow, a fluid phase segregation, a fluid flow discrimination within a conduit, a well integrity monitoring, an in-well leak detection, an annular fluid flow, an overburden monitoring, a fluid flow detection behind a casing, a fluid induced hydraulic fracture detection in an overburden, a sand ingress, a wax deposition, or a sand flow along a wellbore. 
     
     
         3 . The method of  claim 1 , wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO 2  injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events. 
     
     
         4 . The method of  claim 1 , wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth. 
     
     
         5 . The method of  claim 1 , wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range. 
     
     
         6 . The method of  claim 1 , wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile. 
     
     
         7 . The method of  claim 1 , wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval. 
     
     
         8 . The method of  claim 1 , wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself. 
     
     
         9 . The method of  claim 1 , wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal. 
     
     
         10 . The method of  claim 1 , wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal. 
     
     
         11 . The method of  claim 1 , wherein the plurality of temperature features comprises a wavelet transform of the temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth). 
     
     
         12 . The method of  claim 11 , wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof. 
     
     
         13 . The method of  claim 1 , wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth. 
     
     
         14 . The method of  claim 1 , wherein the plurality of temperature features comprises a heat loss parameter. 
     
     
         15 . The method of  claim 1 , wherein the plurality of temperature features comprise a time-depth derivative, a depth-time derivative, or both. 
     
     
         16 . The method of  claim 1 , wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function. 
     
     
         17 . The method of  claim 1 , wherein using the at least one temperature feature and the at least one frequency domain feature comprises:
 using the at least one temperature feature in a first model;   using the at least one frequency domain feature of the one or more frequency domain features in a second model;   combining an output from the first model and an output from the second model to form a combined output; and   determining a presence or extent of the event based on the combined output.   
     
     
         18 . The method of  claim 17 , wherein the first model comprise one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the presence of the event at one or more locations. 
     
     
         19 . The method of  claim 18 , wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication of the presence or extent of the event at the one or more locations. 
     
     
         20 . The method of  claim 19 , wherein combining the output from the first model with the output from the second model comprises determining the combined output as a function of: 1) the output from the first model, and 2) the output from the second model. 
     
     
         21 . The method of  claim 1 , further comprising:
 receiving an independent indication of extent of the event; and   allocating a portion of the event extent to the one or more locations based on the event extent at the one or more locations based on the combined output.   
     
     
         22 . The method of  claim 1 , further comprising:
 receiving the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor or receiving the acoustic signal from a sensor comprising a fiber optic based acoustic sensor.   
     
     
         23 . The method of  claim 22 , wherein the fiber optic based temperature sensor or the fiber optic based acoustic sensor is disposed in a wellbore. 
     
     
         24 . The method of  claim 1 , further comprising:
 denoising and calibrating the temperature sensing signal prior to determining the one or more temperature features; or   normalizing the one or more temperature features prior to determining the presence of the one or more events.   
     
     
         25 . A method of determining a presence or extent of an event, the method comprising:
 determining a plurality of temperature features from a temperature sensing signal, wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, or a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), a heat loss parameter, a time-depth derivative, or a depth-time derivative;   determining one or more frequency domain features from an acoustic signal originated in the wellbore; and   using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine the presence or extent of the event at one or more locations.   
     
     
         26 . The method of  claim 25 , wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: a fluid inflow, a fluid outflow, a fluid phase segregation, a fluid flow discrimination within a conduit, a well integrity monitoring, an in-well leak detection, an annular fluid flow, an overburden monitoring, a fluid flow detection behind a casing, a sand ingress, a wax deposition, or a sand flow along a wellbore. 
     
     
         27 . The method of  claim 25 , wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO 2  injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events. 
     
     
         28 . The method of  claim 25 , wherein the one or more events comprises a fluid inflow at one or more locations. 
     
     
         29 . The method of  claim 28 , wherein the fluid inflow is a liquid inflow at the one or more locations. 
     
     
         30 . The method of  claim 29 , wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both an aqueous liquid and a hydrocarbon liquid. 
     
     
         31 . The method of  claim 25 , wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range. 
     
     
         32 . The method of  claim 25 , wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile. 
     
     
         33 . The method of  claim 25 , wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval. 
     
     
         34 . The method of  claim 25 , wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function. 
     
     
         35 . The method of  claim 25 , wherein using the at least one temperature feature and the at least one frequency domain feature comprises:
 using the at least one temperature features in a first model;   using at least one frequency domain feature of the one or more frequency domain features in a second model;   combining an output from the first model and an output from the second model to form a combined output; and   determining a presence or extent of the event at the one or more locations based on the combined output.   
     
     
         36 . The method of  claim 35 , wherein the first model comprise one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the presence or absence of the event at one or more locations along the wellbore. 
     
     
         37 . The method of  claim 35 , wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication of a presence or an extent thereof at the one or more locations. 
     
     
         38 . The method of  claim 35 , wherein combining the output from the first model with the output from the second model comprises determining the combined output as a function of: 1) the output from the first model, and 2) the output from the second model. 
     
     
         39 . The method of  claim 25 , further comprising:
 receiving an independent indication of an event extent; and   allocating a portion of the event extent to the one or more locations based on the determined event extent at the one or more locations based on the combined output.   
     
     
         40 . A system of determining a presence or extent of an event, the system comprising:
 a processor;   a memory; and   an analysis program stored in the memory, wherein the analysis program is configured, when executed on the processor, to:   receive a temperature sensing signal and an acoustic signal;   determine a plurality of temperature features from the temperature sensing signal;   determine one or more frequency domain features from the acoustics signal; and   determine a presence or extent of the event at one or more locations using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features.   
     
     
         41 . The system of  claim 40 , wherein the analysis program is further configured to:
 use the at least one temperature features in a first model;   use at least one frequency domain feature of the one or more frequency domain features in a second model;   combine an output from the first model and an output from the second model to form a combined output; and   determine a presence or extent of the event at the one or more locations based on the combined output.   
     
     
         42 . The system of  claim 41 , wherein the first model comprises one or more multivariate models, and wherein the output from each multivariate model of the one or more multivariate model comprises an indication of the one or more locations. 
     
     
         43 . The system of  claim 40 , wherein the second model comprises a regression model, and wherein the output from the regression model comprises an indication an extent of the event at the one or more locations. 
     
     
         44 . The system of  claim 41 , wherein the analysis program is further configured to:
 combine the output from the first model with the output from the second model as a function of: 1) the output from the first model, and 2) the output from the second model.   
     
     
         45 . The system of  claim 40 , wherein the analysis program is further configured to:
 receive an independent indication of an event extent; and   allocate a portion of the event extent to the one or more locations based on the determined event extent at the one or more locations based on the combined output.   
     
     
         46 . The system of  claim 40 , wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, or a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), a heat loss parameter, a time-depth derivative, or a depth-time derivative. 
     
     
         47 . The system of  claim 40 , wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range. 
     
     
         48 . The system of  claim 40 , wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile. 
     
     
         49 . The system of  claim 40 , wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval. 
     
     
         50 . The system of  claim 40 , wherein the one or more frequency domain features comprise at least one of: a spectral centroid, a spectral spread, a spectral roll-off, a spectral skewness, an RMS band energy, a total RMS energy, a spectral flatness, a spectral slope, a spectral kurtosis, a spectral flux, or a spectral autocorrelation function.