Automated interpretation of deposition volumes

Abstract

Disclosed are systems, apparatuses, methods, and computer readable medium for modeling depositions within a pipe. A method includes: building a predictive model of an interior of a pipe based on legacy data observations; receiving flowline data from a sensor indicating a flow profile within the pipe; analyzing the flowline data using the predictive model; outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and rendering a representation of the data representing the change in the flow profile.

What is claimed is: 
     
         1 . A method comprising:
 building a predictive model of an interior of a pipe based on legacy data observations;   receiving flowline data from a sensor indicating a flow profile within the pipe;   analyzing the flowline data using the predictive model;   outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and   rendering a representation of the data representing the change in the flow profile.   
     
     
         2 . The method of  claim 1 , wherein the flowline data is collected using a pressure transducer. 
     
     
         3 . The method of  claim 1 , wherein the legacy data observations include data previously captured using at least one pressure transducer, a flowline geometry, or at least one fluid property and storing the legacy data observations in a database as a captured data set. 
     
     
         4 . The method of  claim 3 , wherein the predictive model is segmented based on the at least one pressure transducer, the flowline geometry, or the at least one fluid property. 
     
     
         5 . The method of  claim 1 , further comprising:
 collecting at least one of an initial pressure, an MFR, and incremental MFR, a start time, an end time, a Halland's factor, a friction factor, a pipe diameter, an incremental distance, an incremental diameter, an incremental acoustic velocity, an incremental density, an incremental viscosity, an incremental simulated pressure, an incremental observed pressure, and an incremental calculated deposition.   
     
     
         6 . The method of  claim 1 , further comprising:
 initiating a pressure pulse within a flowline of the pipe; and   measuring the pressure pulse with the sensor.   
     
     
         7 . The method of  claim 6 , wherein the pressure pulse is created through injecting mass, removing mass, or actuating a valve. 
     
     
         8 . The method of  claim 1 , wherein the predictive model includes a linear or non-linear regression model. 
     
     
         9 . The method of  claim 8 , wherein the predictive model is based at least on the following equations: 
       
         
           
             
               
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         where x=x 1 , . . . , x r  have a linear relationship, ε are regression coefficients, and & is random error. 
       
     
     
         10 . The method of  claim 1  wherein the predictive model is run on a programmable logical controller in communication with the sensor. 
     
     
         11 . A system comprising:
 a storage configured to store instructions;   a processor configured to execute the instructions and cause the processor to:   build a predictive model of an interior of a pipe based on legacy data observations;   receive flowline data from a sensor indicating a flow profile within the pipe;   analyze the flowline data using the predictive model;   outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and   render a representation of the data representing the change in the flow profile.   
     
     
         12 . The system of  claim 11 , wherein the flowline data is collected using a pressure transducer. 
     
     
         13 . The system of  claim 11 , wherein the legacy data observations include data previously captured using at least one pressure transducer, a flowline geometry, or at least one fluid property and storing the legacy data observations in a database as a captured data set. 
     
     
         14 . The system of  claim 11 , wherein the processor is configured to execute the instructions and cause the processor to:
 initiate a pressure pulse within a flowline; and   measure the pressure pulse with the sensor.   
     
     
         15 . The system of  claim 11 , wherein the predictive model includes a linear or non-linear regression model. 
     
     
         16 . The system of  claim 11 , wherein the predictive model is run on a programmable logical controller in communication with the sensor. 
     
     
         17 . A non-transitory computer readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to:
 build a predictive model of an interior of a pipe based on legacy data observations;   receive flowline data from a sensor indicating a flow profile within the pipe;   analyze the flowline data using the predictive model;   outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and   render a representation of the data representing the change in the flow profile.   
     
     
         18 . The computer readable medium of  claim 17 , the flowline data is collected using a pressure transducer. 
     
     
         19 . The computer readable medium of  claim 17 , wherein the computer readable medium further comprises instructions that, when executed by the computing system, cause the computing system to:
 initiate a pressure pulse within a flowline; and   measure the pressure pulse with the sensor.   
     
     
         20 . The computer readable medium of  claim 17 , the predictive model includes a linear or non-linear regression model.