# US 2023/0057091 A1 · Predicting internal corrosion in gas flow lines using machine learning

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- Number: US 2023/0057091 A1
- Document ID: US20230057091A1
- Status: Published application
- Kind: A1
- Application number: 17/409,400
- Filed: 2021-08-23
- Published: 2023-02-23
- Earliest claimed: 2021-08-23
- Assignees: Saudi Arabian Oil Company
- Applicants: Saudi Arabian Oil Company
- Inventors: Muhammad Sohaib Khan; Balakoteswara R. Koppuravuri; Sarafudheen M. Tharayil; Fathi BuGubaia; Mohammad S. Al-Qahtani
- Web page: https://patentgrep.com/patent/US20230057091A1

## CPC

- G01N 33/0047: {Organic compounds}
- G01K 13/024: of moving gases
- G06N 3/02: Neural networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G01K: MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR (radiation pyrometry G01J5/00)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

In an example method, a system obtains first data indicating a plurality of properties of a plurality of gas flow lines. The properties include, for each of the gas flow lines (i) data representing a flow rate of a gas through that gas flow line, (ii) data representing a pressure of the gas in that gas flow line, and (iii) data representing an additive included in the gas in that gas flow line, such as a substance for inhibiting corrosion. For each of the gas flow lines, the system uses a computerized neural network to determine a risk of corrosion associated with that gas flow line based on the properties of that gas flow line, determines whether the metric for that gas flow line is greater than a threshold level, and if so, generates a notification for presentation to a user.

## Claims

### Claim 1

A method comprising:
obtaining, using one or more processors, first data indicating a plurality of first properties of a plurality of first gas flow lines, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
data representing a flow rate of a gas through that first gas flow line,
data representing a pressure of the gas in that first gas flow line, and
data representing an additive included in the gas in that first gas flow line,
wherein the additive comprises a substance for inhibiting corrosion in that first gas flow line; and
for each of the plurality of first gas flow lines:
determining, using the one or more processors implementing a computerized neural network, a risk of corrosion associated with that first gas flow line based on the first properties of that first gas flow line,
determining, using the one or more processors, whether the first metric for that first gas flow line is greater than a threshold level, and
responsive to determining that the first metric for that first gas flow line is greater than the threshold level, generating, using the one or more processors, a notification indicating the first metric for that first gas flow line for presentation to a user.

### Claim 2 (depends on 1)

The method of claim 1, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
a composition of the gas in that first gas flow line.

### Claim 3 (depends on 1)

The method of claim 1, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
a composition of that first gas flow line.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
determining that the first data comprises incomplete data regarding one or more of the plurality of first gas flow lines, and
in response to determining that the first data comprises incomplete data regarding the one or more of the plurality of first gas flow lines, modifying the first data to include additional data regarding the one or more of the plurality of first gas flow lines.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
determining that the first data comprises inaccurate data regarding one or more of the plurality of first gas flow lines, and
in response to determining that the first data comprises inaccurate data regarding the one or more of the plurality of first gas flow lines, modifying the first data to correct the inaccurate data regarding the one or more of the plurality of first gas flow lines.

### Claim 6 (depends on 1)

The method of claim 1, wherein the computerized neural network is trained based a plurality of sets of training data, wherein each of the plurality of sets of training data comprises:
second data indicating a plurality of second properties of a plurality of second gas flow lines, wherein the plurality of second properties comprise, for each of the plurality of second gas flow lines:
data representing a flow rate of a gas through that second gas flow line,
data representing a pressure of the gas in that second gas flow line,
data representing a second additive included in the gas in that second gas flow line, wherein the second additive comprises a substance for inhibiting corrosion in that second gas flow line, and
data representing an extent of corrosion in that second gas line.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
for each of the plurality of first gas flow lines, determining a degree of severity of the risk of corrosion associated with that first gas flow line.

### Claim 8 (depends on 7)

The method of claim 7, wherein the plurality of first gas flow lines comprise one or more hydrocarbon gas flow lines.

### Claim 9

A system comprising:
one or more processors; and
one or more non-transitory computer readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining first data indicating a plurality of first properties of a plurality of first gas flow lines, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
data representing a flow rate of a gas through that first gas flow line,
data representing a pressure of the gas in that first gas flow line, and
data representing an additive included in the gas in that first gas flow line, wherein the additive comprises a substance for inhibiting corrosion in that first gas flow line; and
for each of the plurality of first gas flow lines:
determining, using a computerized neural network, a risk of corrosion associated with that first gas flow line based on the first properties of that first gas flow line,
determining whether the first metric for that first gas flow line is greater than a threshold level, and
responsive to determining that the first metric for that first gas flow line is greater than the threshold level, generating a notification indicating the first metric for that first gas flow line for presentation to a user.

### Claim 10 (depends on 9)

The system of claim 9, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
a composition of the gas in that first gas flow line.

### Claim 11 (depends on 9)

The system of claim 9, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
a composition of that first gas flow line.

### Claim 12 (depends on 9)

The system of claim 9, the operations further comprising:
determining that the first data comprises incomplete data regarding one or more of the plurality of first gas flow lines, and
in response to determining that the first data comprises incomplete data regarding the one or more of the plurality of first gas flow lines, modifying the first data to include additional data regarding the one or more of the plurality of first gas flow lines.

### Claim 13 (depends on 9)

The system of claim 9, further comprising:
determining that the first data comprises inaccurate data regarding one or more of the plurality of first gas flow lines, and
in response to determining that the first data comprises inaccurate data regarding the one or more of the plurality of first gas flow lines, modifying the first data to correct the inaccurate data regarding the one or more of the plurality of first gas flow lines.

### Claim 14 (depends on 9)

The system of claim 9, wherein the computerized neural network is trained based a plurality of sets of training data, where each of the plurality of sets of training data comprises:
second data indicating a plurality of second properties of a plurality of second gas flow lines, wherein the plurality of second properties comprise, for each of the plurality of second gas flow lines:
data representing a flow rate of a gas through that second gas flow line,
data representing a pressure of the gas in that second gas flow line,
data representing a second additive included in the gas in that second gas flow line, wherein the second additive comprises a substance for inhibiting corrosion in that second gas flow line, and
data representing an extent of corrosion in that second gas line.

### Claim 15 (depends on 9)

The system of claim 9, the operations further comprising:
for each of the plurality of first gas flow lines, determining a degree of severity of the risk of corrosion associated with that first gas flow line.

### Claim 16 (depends on 15)

The system of claim 15, wherein the plurality of first gas flow lines comprise one or more hydrocarbon gas flow lines.

### Claim 17

One or more non-transitory computer readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
obtaining first data indicating a plurality of first properties of a plurality of first gas flow lines, wherein the plurality of first properties comprise, for each of the plurality of first gas flow lines:
data representing a flow rate of a gas through that first gas flow line,
data representing a pressure of the gas in that first gas flow line, and
data representing an additive included in the gas in that first gas flow line,
wherein the additive comprises a substance for inhibiting corrosion in that first gas flow line; and
for each of the plurality of first gas flow lines:
determining, using a computerized neural network, a risk of corrosion associated with that first gas flow line based on the first properties of that first gas flow line,
determining whether the first metric for that first gas flow line is greater than a threshold level, and
responsive to determining that the first metric for that first gas flow line is greater than the threshold level, generating a notification indicating the first metric for that first gas flow line for presentation to a user.