Detecting operational anomalies for continuous hydraulic fracturing monitoring

Abstract

A method for detecting operating anomalies during hydraulic fracturing includes inducing tube waves in a well during pumping a hydraulic fracture treatment. At least one of pressure and time derivative of pressure in the well is measured. The measured at least one of pressure and time derivative of pressure is transformed into the cepstrum domain. An operational anomaly is detected by determining a change in cepstral quefrency corresponding to a two-way travel time of the tube waves and resonances in the well.

What is claimed is: 
     
         1 . A method for detecting operating anomalies during hydraulic fracturing, comprising:
 inducing tube waves in a well during pumping a hydraulic fracture treatment;   measuring at least one of pressure and time derivative of pressure in the well;   transforming the measured at least one of pressure and time derivative of pressure into the cepstrum domain; and   detecting an operational anomaly by determining a change in cepstral quefrency corresponding to a two-way travel time of the tube waves in the well.   
     
     
         2 . The method of  claim 1  wherein the change in cepstral quefrency comprises a maximum value of quefrency. 
     
     
         3 . The method of  claim 1  wherein the change in cepstral quefrency comprises a minimum value of quefrency. 
     
     
         4 . The method of  claim 1  wherein the change in cepstral quefrency comprises a sum of a maximum and a minimum value of quefrency. 
     
     
         5 . The method of  claim 1  wherein the change in cepstral quefrency comprises at least one of peak width, rise time and time offset. 
     
     
         6 . The method of  claim 1  wherein the inducing tube waves comprises changing a rate of pumping the hydraulic fracture treatment so as to induce water hammer. 
     
     
         7 . The method of  claim 1  wherein the inducing tube waves comprises imparting pressure changes into the well. 
     
     
         8 . The method of  claim 1  wherein on determining the operational anomaly, a warning is communicated to a system operator, the method further comprising performing a mitigation activity corresponding to the determined anomaly. 
     
     
         9 . The method of  claim 8  wherein the mitigation activity includes comprises changing at least one parameter of a hydraulic fracture treatment. 
     
     
         10 . The method of  claim 9  wherein the at least one mitigation parameter comprises at least one of proppant concentration, proppant density, proppant amount, proppant particle size distribution, proppant particle shape, fluid type/composition, fluid viscosity, fluid viscosity change rate, fluid pumping rate, fluid temperature, fluid chemical composition, chemical additives, co-injection of energized gases in both liquid and gas phases, injection of petroleum distillates, pH of injection fluid, fluid pumping pressure, diverter type, perforation location, number of perforations, angle of perforations, size of perforations, depth of perforations, plug type, and stage length. 
     
     
         11 . The method of  claim 9  wherein the monitoring and mitigation steps are controlled by a microcomputer. 
     
     
         12 . The method of  claim 9  wherein a machine learning algorithm is applied to identify types of pumping problems and suggest solutions. 
     
     
         13 . The method of  claim 1  wherein a visual tracking is provided to a system operator. 
     
     
         14 . The method of  claim 1  wherein the operational anomaly comprises screenout. 
     
     
         15 . A non-transitory computer readable medium comprising logic operable to cause a computer to perform actions comprising:
 accepting as input to the computer, signals resulting from inducing tube waves in a well during pumping a hydraulic fracture treatment and measuring at least one of pressure and time derivative of pressure in the well;   transforming the measurements of at least one of pressure and time derivative of pressure into the cepstrum domain; and   detecting an operational anomaly by determining a change in cepstral quefrency corresponding to a two-way travel time of the tube waves in the well.   
     
     
         16 . The non-transitory computer readable medium of  claim 15  wherein the change in cepstral quefrency comprises a maximum value of quefrency. 
     
     
         17 . The non-transitory computer readable medium of  claim 15  wherein the change in cepstral quefrency comprises a minimum value of quefrency. 
     
     
         18 . The non-transitory computer readable medium of  claim 15  wherein the change in cepstral quefrency comprises a sum of a maximum and a minimum value of quefrency. 
     
     
         19 . The non-transitory computer readable medium of  claim 15  wherein the change in cepstral quefrency comprises at least one of peak width, rise time and time offset. 
     
     
         20 . The non-transitory computer readable medium of  claim 15  wherein the inducing tube waves comprises changing a rate of pumping the hydraulic fracture treatment so as to induce water hammer. 
     
     
         21 . The non-transitory computer readable medium of  claim 15  wherein the inducing tube waves comprises imparting pressure changes into the well. 
     
     
         22 . The non-transitory computer readable medium of  claim 15  further comprising logic operable to cause the computer to, on determining the operational anomaly, communicating a warning to a system operator. 
     
     
         23 . The non-transitory computer readable medium of  claim 22  further comprising logic operable to cause the computer to calculate a mitigation parameter to correct the operational anomaly. 
     
     
         24 . The non-transitory computer readable medium of  claim 23  wherein the at least one mitigation parameter comprises at least one of proppant concentration, proppant density, proppant amount, proppant particle size distribution, proppant particle shape, fluid type/composition, fluid viscosity, fluid viscosity change rate, fluid pumping rate, fluid temperature, fluid chemical composition, chemical additives, co-injection of energized gases in both liquid and gas phases, injection of petroleum distillates, pH of injection fluid, fluid pumping pressure, diverter type, perforation location, number of perforations, angle of perforations, size of perforations, depth of perforations, plug type, and stage length. 
     
     
         25 . The non-transitory computer readable medium of  claim 24  further comprising logic operable to cause the computer to implement a machine learning algorithm to identify types of pumping problems and suggest solutions. 
     
     
         26 . The non-transitory computer readable medium of  claim 15  wherein the operational anomaly comprises screenout.