Corrosion prediction methods and systems

Abstract

A hybrid model for predicting corrosion in a system integrates a physics-based model developed using laboratory data and a machine-learning model developed using in-field data. Said hybrid model may be used, for example, in methods by: determining a physics-based measurement of corrosion using a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab-based measurements; determining a machine learning-based measurement of corrosion using a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field-based measurements; and applying an ensemble method to the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to yield an estimated measure of corrosion of the substrate. The hybrid model may be applied to corrosion mechanisms that occur in, for example, hydrocarbon transportation systems, hydrocarbon production systems, hydrocarbon refining systems, and alkylation systems.

The invention claimed is: 
     
         1 . A method comprising:
 determining, via the computing system, a physics-based measurement of corrosion using a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab-based measurements, wherein the physics-based model correlates (a) the physics-based measurement of corrosion to (b) an operational parameter;   determining, via the computing system, a machine learning-based measurement of corrosion using a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field-based measurements, wherein the machine-learning model correlates (a) the machine learning-based measurement of corrosion to (b) the physics-based measurement of corrosion and the operational parameters; and   applying, via the computing system, an ensemble method to the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to yield an estimated measure of corrosion of the substrate.   
     
     
         2 . The method of  claim 1 , wherein the operational parameter is selected from the group consisting of: a total acid number (TAN) of the fluid, a composition of the TAN, a total reactive sulfur (TRS) of the fluid, a composition of the TRS, an origin of the fluid, a composition of the fluid, a temperature of the fluid, a fluid density, a fluid velocity, a corrosion inhibitor concentration in the fluid, a corrosion inhibitor composition, composition of the substrate, configuration of the substrate, phases of the fluid, a phase behavior of the fluid, an absence or presence of scale on the substrate, a composition of said scale, a density of said scale, and any combination thereof. 
     
     
         3 . The method of  claim 1  further comprising:
 repairing and/or replacing a component comprising the substrate based on the estimated measure of corrosion. 
 
     
     
         4 . The method of  claim 1  further comprising:
 building a system or portion thereof comprising a component that comprises the substrate, wherein a composition of the substrate is chosen based on the estimated measure of corrosion. 
 
     
     
         5 . The method of  claim 1  further comprising:
 refining a feedstock, wherein the feedstock or a downstream product and/or distillate thereof is the fluid; 
 measuring the operational parameter in real-time; and 
 monitoring the estimated measure of corrosion over time. 
 
     
     
         6 . The method of  claim 1  further comprising:
 refining a feedstock, wherein the feedstock or a downstream product and/or distillate thereof is the fluid; 
 measuring the operational parameter in real-time; and 
 changing a composition of the feedstock based on the estimated measure of corrosion over time. 
 
     
     
         7 . The method of  claim 1  further comprising:
 refining a feedstock, wherein the feedstock or a downstream product and/or distillate thereof is the fluid; and 
 projecting the estimated measure of corrosion based on a change to the operational parameter. 
 
     
     
         8 . A method for predicting corrosion comprising:
 providing a hybrid model that correlates two or more operational parameters to an estimated measure of corrosion comprising:
 a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab measurements, wherein the physics-based model correlates (a) the physics-based measurement of corrosion to (b) two or more operational parameters; 
 a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field measurements, wherein the machine-learning model correlates (a) a machine learning-based measurement of corrosion to (b) the physics-based measurement of corrosion and the two or more operational parameters; 
 an ensemble method that correlates (a) the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to (b) the estimated measure of corrosion; 
   simulating values or ranges of values in the hybrid model for a first operational parameter of the two or more operational parameters and the estimated measure of corrosion; and   generating a value or range of values for a second operational parameter of the two or more operational parameters.   
     
     
         9 . The method of  claim 8 , wherein at least one of the two or more operational parameters are selected from the group consisting of: a total acid number (TAN) of the fluid, a composition of the TAN, a total reactive sulfur (TRS) of the fluid, a composition of the TRS, an origin of the fluid, a composition of the fluid, a temperature of the fluid, a fluid density, a fluid velocity, a corrosion inhibitor concentration in the fluid, a corrosion inhibitor composition, composition of the substrate, configuration of the substrate, phases of the fluid, a phase behavior of the fluid, an absence or presence of scale on the substrate, a composition of said scale, a density of said scale, and any combination thereof. 
     
     
         10 . The method of  claim 8 , wherein the second operational parameter is a composition of the substrate, wherein the substrate corresponds to a component in a system, and wherein the method further comprises:
 using the component having the composition in the system.   
     
     
         11 . The method of  claim 8 , wherein the second operational parameter is a composition of the fluid, wherein the substrate corresponds to a component in a system, and wherein the method further comprises:
 sourcing a feedstock for the system based on the value or range of values for a second operational parameter.   
     
     
         12 . A computing system comprising: a processor; a non-transitory, computer-readable medium comprising a hybrid model that correlates one or more operational parameters to an estimated measure of corrosion; a non-transitory, computer-readable medium comprising instructions configured to accept inputs that include one or more operational parameters and/or an estimated measure of corrosion; and run the hybrid model to produce an output that includes one or more operational parameters and/or an estimated measure of corrosion that are not inputs wherein the hybrid model comprises:
 a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab measurements, wherein the physics-based model correlates (a) the physics-based measurement of corrosion to (b) the one or more operational parameters;   a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field measurements, wherein the machine-learning model correlates (a) a machine learning-based measurement of corrosion to (b) the physics-based measurement of corrosion and the one or more operational parameters; and   an ensemble method that correlates (a) the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to (b) the estimated measure of corrosion.   
     
     
         13 . The computing system of  claim 12 , wherein the output comprises a value or range of values for an operational parameter that is not an input. 
     
     
         14 . The computing system of  claim 12 , wherein the output comprises the estimated measure of corrosion that is not an input. 
     
     
         15 . The computing system of  claim 12 , wherein at least one of the one or more operational parameters are selected from the group consisting of: a total acid number (TAN) of the fluid, a composition of the TAN, a total reactive sulfur (TRS) of the fluid, a composition of the TRS, an origin of the fluid, a composition of the fluid, a temperature of the fluid, a fluid density, a fluid velocity, a corrosion inhibitor concentration in the fluid, a corrosion inhibitor composition, composition of the substrate, configuration of the substrate, phases of the fluid, a phase behavior of the fluid, an absence or presence of scale on the substrate, a composition of said scale, a density of said scale, and any combination thereof.