Systems and methods for application of statistical classification and pattern recognition for compartment design in horizontal oil wells

1 . A method for application of statistical classification and pattern recognition for compartment design in horizontal oil wells comprising:

receiving, by a computing device, an input parameter for a target well, wherein the input parameter includes a permeability log for the target well;

performing, by the computing device, a log transformation on the permeability log to create transformed data;

calculating, by the computing device, a mean and a standard deviation of the transformed data;

generating, by the computing device, a classification flag that classifies the permeability log, based on the standard deviation;

classifying, by the computing device, noise from the permeability log;

transforming, by the computing device, the noise based on a predefined pattern library;

creating, by the computing device, a final transformed signal from the classification flag and the noise;

generating, by the computing device, a compartment design from the final transformed signal that provides a recommended compartment interval versus measured depth of the target well;

calculating, by the computing device, a maximum compartment interval that can be physically installed in the target well, wherein the maximum compartment interval is a function of a joint length of completions tubing received as user input, wherein the maximum compartment interval, Imax is calculated as:

I

max

=

⌈

N

S

*

J

⌉

,

where “J” is a joint length given in feet, “S” is a number of samples per feet and “N” is a total number of data sample values in an input permeability array; and

drilling, using drilling equipment, the target well based on the compartment design and the maximum compartment interval.

2 . The method of claim 1 , wherein the input parameter further includes at least one of the following: a well target entry depth, a length of a completion tubing joint, a minimum length of a compartment, or a permeability cut-off value.

3 . The method of claim 1 , wherein the classification flag is created at a log sampling resolution.

4 . The method of claim 1 , further comprising transforming the classification flag to a resolution of one joint length of a completion design.

5 . The method of claim 1 , wherein performing the log transformation includes performing a statistical analysis of permeability log for the target well.

6 . The method of claim 1 , wherein the noise is classified based on a pattern of interval−1 and interval+1 value.

7 . The method of claim 1 , further comprising receiving user input of a permeability cut off value and values below a predetermined cutoff are assigned a class −4, which identifies blank pipe compartment intervals.

8 . The method of claim 1 , further comprising smoothing the classification flag using a sliding window with a selected window size equal to about 1 joint length.

9 . The method of claim 1 , further comprising grouping similar magnitudes of permeability based on standard-deviation based classification to create the final transformed signal.

10 . The method of claim 1 , further comprising defining a reservoir isolation packer, an inflow completion device, and a nozzle inflow control device (NICD).

11 . A system for application of statistical classification and pattern recognition for compartment design in horizontal oil wells comprising:

drilling equipment for drilling a target well, wherein the target well is a horizontal well, wherein the drilling equipment includes a special purpose computing device that includes a memory component that stores logic, that when executed by the special purpose computing device, causes the system to perform at least the following:

receive an input parameter for the target well, wherein the input parameter includes a permeability log for the target well;

perform a log transformation on the permeability log to create transformed data;

calculate a mean and a standard deviation of the transformed data;

generate a classification flag that classifies the permeability log, based on the standard deviation;

classify noise from the permeability log;

transform the noise based on a predefined pattern library;

create a final transformed signal from the classification flag and the noise;

generate a compartment design from the final transformed signal that provides recommended compartment intervals versus measured depth of the target well;

calculate a maximum compartment interval that can be physically installed in the target well, wherein the maximum compartment interval is a function of a joint length of completions tubing received as user input, wherein the maximum compartment interval, Imax is calculated as:

I

max

=

⌈

N

S

*

J

⌉

,

where “J” is a joint length given in feet, “S” is a number of samples per feet and “N” is a total number of data sample values in an input permeability array; and

drill, using the drilling equipment, the target well based on the compartment design and the maximum compartment interval.

12 . The system of claim 11 , wherein the input parameter further includes at least one of the following: a well target entry depth, a length of a completion tubing joint, a minimum length of a compartment, or a permeability cut-off value.

13 . The system of claim 11 , wherein the noise is classified based on a pattern of interval−1 and interval+1 value.

14 . The system of claim 11 , wherein the logic further causes the system to receive user input of a permeability cut off value and values below a predetermined cutoff are assigned a class−4, which identifies blank pipe compartment intervals.

15 . Drilling equipment for application of statistical classification and pattern recognition for compartment design in horizontal oil wells comprising:

a drill for drilling a target well, wherein the target well is a horizontal well; and

a computing device that includes a memory component that stores logic, that when executed by the computing device, causes the computing device to perform at least the following:

receive an input parameter for the target well, wherein the input parameter includes a permeability log for the target well;

perform a log transformation on the permeability log to create transformed data;

calculate a mean and a standard deviation of the transformed data;

generate a classification flag that classifies the permeability log, based on the standard deviation;

classify noise from the permeability log;

transform the noise based on a predefined pattern library;

create a final transformed signal from the classification flag and the noise;

generate a compartment design from the final transformed signal that provides recommended compartment intervals versus measured depth of the target well; calculate a maximum compartment interval that can be physically installed in the target well, wherein the maximum compartment interval is a function of a joint length of completions tubing received as user input, wherein the maximum compartment interval, Imax is calculated as:

I

max

=

⌈

N

S

*

J

⌉

,

where “J” is a joint length given in feet, “S” is a number of samples per feet and “N” is a total number of data sample values in an input permeability array; and

drill, using the drill, the target well based on the compartment design and the maximum compartment interval.