# US 2014/0195215 A1 · Modeling wellbore fluids

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- Number: US 2014/0195215 A1
- Document ID: US20140195215A1
- Status: Published application
- Kind: A1
- Application number: 13/735,756
- Filed: 2013-01-07
- Published: 2014-07-10
- Earliest claimed: 2013-01-07
- Assignees: Halliburton Energy Services, Inc.
- Applicants: Halliburton Energy Services, Inc.
- Inventors: Dingding Chen; David L. Perkins; Christopher Michael Jones; Li Gao; Lucas Fontenelle; Johanna Haggstrom
- Web page: https://patentgrep.com/patent/US20140195215A1

## CPC

- E21B 43/26: by forming crevices or fractures
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06F 30/28: using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
- G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06F 2111/10: Numerical modelling
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Techniques for modeling a wellbore fluid that includes a base fluid and one or more fluid additives includes identifying a target viscosity profile of the wellbore fluid; determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile; determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives; comparing the computed viscosity profile and at least one of the computed set of values with a specified criteria of the wellbore fluid; and preparing, based on the comparison, an output including the computed viscosity profile and at least one of the computed set of values of a resultant wellbore fluid.

## Claims

### Claim 1

A computer-implemented method of modeling a wellbore fluid that comprises a base fluid and one or more fluid additives, the method comprising:
identifying a target viscosity profile of the wellbore fluid;
determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile;
determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives;
comparing at least one of the computed viscosity profile or the computed set of values with a specified criteria of the wellbore fluid; and
preparing, based on the comparison, an output comprising at least one of the computed viscosity profile or the computed set of values.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
identifying one or more properties of the base fluid, where identifying a target viscosity profile of the wellbore fluid comprises identifying a target viscosity profile of the wellbore fluid based at least in part on the properties of the base fluid.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
determining at least one parameter of the base fluid based at least in part on the target viscosity profile.

### Claim 4 (depends on 1)

The method of claim 1, wherein determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives comprises:
determining, with a neural network forward model, the computed viscosity profile of the wellbore fluid based, at least in part, on the initial set of values; and
determining, with a neural network inverse model, the computed set of values of the fluid additives and the at least one parameter of the resultant fluid based, at least in part, on the initial set of values.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
training the neural network with historical data comprising viscosity profiles of the wellbore fluid that comprises a conventional water source as the base fluid.

### Claim 6 (depends on 1)

The method of claim 1, wherein determining the initial set of values further comprises identifying the initial set of values utilizing a design of experiments database.

### Claim 7 (depends on 1)

The method of claim 1, wherein the computed set of values is further based on the target viscosity profile or the computed viscosity profile.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
determining, based on the comparison, that the computed set of values are within a threshold tolerance of the initial set of values.

### Claim 9 (depends on 1)

The method of claim 1, further comprising:
determining, based on the comparison, that at least one of the computed set of values is not within a threshold tolerance of the initial set of values;
adjusting the computed set of values utilizing a genetic algorithm; and
determining, using a neural network forward model, an updated viscosity profile of the wellbore fluid based on at least the adjusted set of values.

### Claim 10 (depends on 6)

The method of claim 6, wherein utilizing the genetic algorithm further comprises:
encoding the computed set of values as n-bit strings;
selecting one or more n-bit strings;
mutating one or more bits of at least one of the selected n-bit strings; and
crossing-over at least two of the selected n-bit strings.

### Claim 11 (depends on 1)

The method of claim 1, wherein the one or more properties of the base fluid comprise ion content concentration levels,
the at least one of parameter of the resultant wellbore fluid comprises a pH level, and
the fluid additives comprise a cross-linking constituent, a stabilizer constituent, and a breaker constituent.

### Claim 12

A computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
identifying a target viscosity profile of the wellbore fluid;
determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile;
determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives;
comparing at least one of the computed viscosity profile or the computed set of values with a specified criteria of the wellbore fluid; and
preparing, based on the comparison, an output comprising at least one of the computed viscosity profile or the computed set of values of a resultant wellbore fluid.

### Claim 13 (depends on 12)

The computer storage medium of claim 12, wherein the operations further comprise:
identifying one or more properties of the base fluid, where identifying a target viscosity profile of the wellbore fluid comprises identifying a target viscosity profile of the wellbore fluid based at least in part on the properties of the base fluid.

### Claim 14 (depends on 12)

The computer storage medium of claim 12, wherein the operations further comprise:
determining at least one parameter of the resultant wellbore fluid based at least in part on the target viscosity profile.

### Claim 15 (depends on 12)

The computer storage medium of claim 12, wherein determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives comprises:
determining, with a neural network forward model, the computed viscosity profile of the wellbore fluid based, at least in part, on the initial set of values; and
determining, with a neural network inverse model, the computed set of values of the fluid additives and the at least one parameter of the resultant fluid based, at least in part, on the initial set of values.

### Claim 16 (depends on 15)

The computer storage medium of claim 15, wherein the operations further comprise:
training the neural network with historical data comprising viscosity profiles of the wellbore fluid that comprises a conventional water source as the base fluid.

### Claim 17 (depends on 12)

The computer storage medium of claim 12, wherein determining the initial set of values further comprises identifying the initial set of values utilizing a design of experiments database.

### Claim 18 (depends on 12)

The computer storage medium of claim 12, wherein the computed set of values is further based on the target viscosity profile or the computed viscosity profile.

### Claim 19 (depends on 12)

The computer storage medium of claim 12, wherein the operations further comprise:
determining, based on the comparison, that the computed set of values are within a threshold tolerance of the initial set of values.

### Claim 20 (depends on 12)

The computer storage medium of claim 12, wherein the operations further comprise:
determining, based on the comparison, that at least one of the computed set of values is not within a threshold tolerance of the initial set of values;
adjusting the computed set of values utilizing a genetic algorithm; and
determining, using a neural network forward model, an updated viscosity profile of the wellbore fluid based on at least the adjusted set of values.

### Claim 21 (depends on 17)

The computer storage medium of claim 17, wherein utilizing the genetic algorithm further comprises:
encoding the computed set of values as n-bit strings;
selecting one or more n-bit strings;
mutating one or more bits of at least one of the selected n-bit strings; and
crossing-over at least two of the selected n-bit strings.

### Claim 22 (depends on 12)

The computer storage medium of claim 12, wherein the one or more properties of the base fluid comprise gelling agents and ion content concentration levels,
the at least one of parameter of the resultant wellbore fluid comprises a pH level, and
the fluid additives comprise a cross-linking constituent, a stabilizer constituent, and a breaker constituent.

### Claim 23

A system of one or more computers configured to perform operations comprising:
identifying a target viscosity profile of the wellbore fluid;
determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile;
determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives;
comparing the computed viscosity profile and at least one of the computed set of values with a specified criteria of the wellbore fluid; and
preparing, based on the comparison, an output comprising the computed viscosity profile and at least one of the computed set of values of a resultant wellbore fluid.

### Claim 24 (depends on 23)

The system of claim 23, wherein the operations further comprise:
identifying one or more properties of the base fluid, where identifying a target viscosity profile of the wellbore fluid comprises identifying a target viscosity profile of the wellbore fluid based at least in part on the properties of the base fluid.

### Claim 25 (depends on 23)

The system of claim 23, wherein the operations further comprise:
determining at least one parameter of the resultant fluid based at least in part on the target viscosity profile.

### Claim 26 (depends on 23)

The system of claim 23, wherein determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives comprises:
determining, with a neural network forward model, the computed viscosity profile of the wellbore fluid based, at least in part, on the initial set of values; and
determining, with a neural network inverse model, the computed set of values of the fluid additives and the at least one parameter of the resultant wellbore fluid based, at least in part, on the initial set of values.

### Claim 27 (depends on 26)

The system of claim 26, wherein the operations further comprise:
training the neural network with historical data comprising viscosity profiles of the wellbore fluid that comprises a conventional water source as the base fluid.

### Claim 28 (depends on 23)

The system of claim 23, wherein determining the initial set of values further comprises identifying the initial set of values utilizing a design of experiments database.

### Claim 29 (depends on 12)

The system of claim 12, wherein the computed set of values is further based on the target viscosity profile or the computed viscosity profile.

### Claim 30 (depends on 23)

The system of claim 23, wherein the operations further comprise:
determining, based on the comparison, that the computed set of values are within a threshold tolerance of the initial set of values.

### Claim 31 (depends on 23)

The system of claim 23, wherein the operations further comprise:
determining, based on the comparison, that at least one of the computed set of values is not within a threshold tolerance of the initial set of values;
adjusting the computed set of values utilizing a genetic algorithm; and
determining, using a neural network forward model, an updated viscosity profile of the wellbore fluid based on at least the adjusted set of values.

### Claim 32 (depends on 28)

The system of claim 28, wherein utilizing the genetic algorithm further comprises:
encoding the computed set of values as n-bit strings;
selecting one or more n-bit strings;
mutating one or more bits of at least one of the selected n-bit strings; and
crossing-over at least two of the selected n-bit strings.

### Claim 33 (depends on 23)

The system of claim 23, wherein the one or more properties of the base fluid comprise gelling agents and ion content concentration levels,
the at least one of parameter of the resultant wellbore fluid comprises a pH level, and
the fluid additives comprise a cross-linking constituent, a stabilizer constituent, and a breaker constituent.