Event detection using dts features

Abstract

A method of detecting one or more events comprises determining a plurality of temperature features from a temperature sensing signal, using the plurality of temperature features in an event detection model, and determining the presence or absence of the one or more events at one or more locations based on an output from the event detection model.

1 . A method of detecting one or more events, the method comprising:
 obtaining a temperature sensing signal;   determining a plurality of temperature features from the temperature sensing signal; and   determining the presence of the one or more events using the plurality of temperature features.   
     
     
         2 . The method of  claim 1 , further comprising:
 using the plurality of temperature features in an event detection model, wherein determining the presence of the one or more events comprises: determining the presence of the one or more events based on an output from the event detection model.   
     
     
         3 . 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: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, sand ingress, wax deposition, or sand flow along a wellbore. 
     
     
         4 . 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 darn monitoring events. 
     
     
         5 . The method of  claim 1 , wherein the one or more events comprises a fluid inflow at one or more locations. 
     
     
         6 . The method of  claim 5 , wherein the fluid inflow comprises a liquid inflow at the one or more locations. 
     
     
         7 . The method of  claim 6 , wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both. 
     
     
         8 . The method of  1 , wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth. 
     
     
         9 . 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. 
     
     
         10 . 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. 
     
     
         11 . 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. 
     
     
         12 . 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. 
     
     
         13 . The method of  claim 1 , wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal. 
     
     
         14 . The method of  claim 1 , wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal. 
     
     
         15 . 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). 
     
     
         16 . The method of  claim 15 , wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof. 
     
     
         17 . The method of  claim 1 , wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein. 
     
     
         18 . The method of  claim 1 , wherein the plurality of temperature features comprises a heat loss parameter. 
     
     
         19 . The method of  claim 2 , wherein the event detection model comprises a plurality of models, wherein each model of the plurality of model uses one or more temperature features of the plurality of temperature features, and wherein determining the presence of the event comprises:
 combining an output from each model to determine combined output;   comparing the combined output with an event threshold; and   determining that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.   
     
     
         20 . The method of  claim 19 , wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model. 
     
     
         21 . The method of  claim 2 , wherein the event detection model uses an unsupervised learning algorithm. 
     
     
         22 . The method of  claim 2 , wherein the event detection model uses a supervised learning algorithm. 
     
     
         23 . The method of  claim 1 , further comprising:
 receiving the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.   
     
     
         24 . The method of  claim 23 , wherein the sensor is disposed in a wellbore. 
     
     
         25 . The method of  claim 1 , further comprising:
 denoising the temperature sensing signal prior to determining the one or more temperature features.   
     
     
         26 . The method of  claim 25 , wherein denoising the temperature sensing signal comprises median filtering the temperature sensing signal 
     
     
         27 . The method of  claim 1 , further comprising:
 calibrating the temperature sensing signal.   
     
     
         28 . The method of  claim 1 , further comprising:
 normalizing the one or more temperature features prior to determining the presence of the one or more events.   
     
     
         29 . The method of  claim 1 , wherein determining the presence or absence of the one or more events comprises:
 identifying one or more anomalies in the temperature sensing signal using the one or more temperature features; and/or   selecting depth intervals of the one or more anomalies as event locations.   
     
     
         30 . The method of  claim 1 , further comprising:
 determining a response or remediation procedure based on the presence of the one or more events; and   performing the response or remediation procedure.   
     
     
         31 . A method of detecting one or more events, 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, 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 and/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), or a heat loss parameter; and   determining the presence or absence of the one or more events using the plurality of temperature features.   
     
     
         32 . The method of  claim 31 , 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 fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, sand ingress, wax deposition, or sand flow along a wellbore. 
     
     
         33 . The method of  claim 31 , 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. 
     
     
         34 . The method of  claim 31 , wherein the one or more events comprises a fluid inflow at one or more locations. 
     
     
         35 . The method of  claim 34 , wherein the fluid inflow is a liquid inflow at the one or more locations. 
     
     
         36 . The method of  claim 35 , wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both an aqueous liquid and a hydrocarbon liquid. 
     
     
         37 . The method of  claim 31 , 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. 
     
     
         38 . The method of a  claim 31 , 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. 
     
     
         39 . The method of  claim 31 , 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. 
     
     
         40 . A system of determining one or more events, 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; 
 determine a plurality of temperature features from the temperature sensing signal; and 
 determine the presence of the one or more events using the plurality of temperature features. 
   
     
     
         41 . The system of  claim 40 , wherein the analysis program is further configured to:
 use the plurality of temperature features in an event detection model; and   determine the presence of the one or more events based on an output from the event detection model.   
     
     
         42 . The system of  claim 40 , wherein the processor is further configured to:
 identify one or more event locations using the one or more temperature features.   
     
     
         43 . The system of  claim 40 , wherein the one or more events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, wax deposition, sand ingress, or sand flow along a wellbore. 
     
     
         44 . The system of  claim 40 , 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. 
     
     
         45 . The system of  claim 40 , wherein the one or more events comprise a fluid inflow at one or more locations. 
     
     
         46 . The system of  claim 45 , wherein the fluid inflow is a liquid inflow at the one or more locations. 
     
     
         47 . The system of  claim 46 , wherein the liquid inflow comprises an inflow rate for an aqueous liquid, a hydrocarbon liquid, or a combination of both. 
     
     
         48 . The system of  claim 40 , wherein the processor is further configured to: calibrate the temperature sensing signal. 
     
     
         49 . The system of  claim 40 , wherein the processor is further configured to:
 normalize the one or more temperature features prior to determining the presence or absence of the one or more events.   
     
     
         50 . The system of  claim 40 , wherein the processor is further configured to:
 identify a background event signature using the temperature sensing signal: and   remove the background event signature from the temperature sensing signal prior to determining the plurality of temperature features.   
     
     
         51 . The system of  claim 40 , wherein the processor is further configured to:
 identify one or more anomalies in the temperature sensing signal using the one or more temperature features: and/or   select depth intervals of the one or more anomalies as event locations.   
     
     
         52 . The system of  claim 40 , wherein the processor is further configured to:
 determine a response or remediation procedure based on the presence or absence of the one or more events; and   optionally perform the response or remediation procedure.   
     
     
         53 . The system of  claim 40 , wherein the processor is further configured to:
 determine a confidence level for the determination of the presence of the one or more events; and   optionally perform a remediation procedure based on the confidence level.   
     
     
         54 . The system of  claim 40 , wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth. 
     
     
         55 . The system of  claim 40 , 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. 
     
     
         56 . The system of  claim 40 , 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. 
     
     
         57 . The system of  claim 40 , 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. 
     
     
         58 . The system of  claim 40 , wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself. 
     
     
         59 . The system of  claim 40 , wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal. 
     
     
         60 . The system of  claim 40 , wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal. 
     
     
         61 . The system of  claim 40 , wherein the plurality of temperature features comprises a wavelet transform of the distributed temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth). 
     
     
         62 . The system of  claim 61 , wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof. 
     
     
         63 . The system of  claim 40 , wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein 
     
     
         64 . The system of  claim 40 , wherein the plurality of temperature features comprises a heat loss parameter. 
     
     
         65 . The system of  claim 41 , wherein the event detection model comprises a plurality of models, wherein each model of the plurality of models uses one or more temperature features of the plurality of temperature features, and wherein the analysis program is further configured to: combine an output from each model to determine combined output,
 compare the combined output with an event threshold; and   determine that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.   
     
     
         66 . The system of  claim 65 , wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model. 
     
     
         67 . The system of  claim 41 , wherein the event detection model uses an unsupervised learning algorithm. 
     
     
         68 . The system of  claim 41 , wherein the event detection model uses a supervised learning algorithm. 
     
     
         69 . The system of  claim 40 , wherein the analysis program is further configured to:
 receive the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.   
     
     
         70 . The system of  claim 69 , wherein the sensor is disposed in a wellbore.