Apps
~~''Drowning in Data, Starving for Information''~~

Measurement technology is advancing in the oil and gas industry. Innovations such as wireless transmitters, ~~reduced cost of measurement technology, and increased regulations that require active monitoring have ~~the ~~effect~~ of ~~increasing the number of available measurements~~.
~~! https://www.apmonitor.com~~

! The principal input to the model is the voltage to the motor.

## Wire in Pipe for Drill Shaft Communication

## Apps.DrillComm History

Hide minor edits - Show changes to output

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t[2:10] = t[1:9] + 10 ! temperature increases by ~~5°C~~ with each segment

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t[2:10] = t[1:9] + 10 ! temperature increases by 10°C with each segment

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Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by IntelliServ, a ~~JV~~ between Slumberger and NOV. The model describes the behavior of the communication platform in transmitting signals from the Bottom Hole Assembly (BHA) to the top-side computers.

to:

Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by IntelliServ, a joint venture between Slumberger and NOV. The model describes the behavior of the communication platform in transmitting signals from the Bottom Hole Assembly (BHA) to the top-side computers.

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(:keywords upstream, wire in pipe, mud pulsing, mud pulse, Slumberger, ~~Intelliserv~~, NOV:)

to:

(:keywords upstream, wire in pipe, mud pulsing, mud pulse, Slumberger, IntelliServ, NOV:)

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Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by ~~Intelliserv~~, a JV between Slumberger and NOV. The model describes the behavior of the communication platform in transmitting signals from the Bottom Hole Assembly (BHA) to the top-side computers.

to:

Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by IntelliServ, a JV between Slumberger and NOV. The model describes the behavior of the communication platform in transmitting signals from the Bottom Hole Assembly (BHA) to the top-side computers.

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Measurement technology is advancing in the oil and gas industry. Innovations such as wireless transmitters

to:

Measurement technology is advancing in the oil and gas industry. Innovations such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring have the effect of increasing the number of available measurements. Increased bandwidth does not necessarily lead to improved operations. Some describe this as ''Drowning in Data, Starving for Information''.

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Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by Intelliserv, a JV between Slumberger and NOV.

to:

Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by Intelliserv, a JV between Slumberger and NOV. The model describes the behavior of the communication platform in transmitting signals from the Bottom Hole Assembly (BHA) to the top-side computers.

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Contact support@apmonitor.com to learn more about Advanced Process Monitoring for upstream drilling and production systems.

Contact support@apmonitor.com to learn more about Advanced Process Monitoring for upstream drilling and production systems.

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(:title Wire in Pipe for Drill Shaft Communication:)

(:keywords upstream, wire in pipe, mud pulsing, mud pulse, Slumberger, Intelliserv, NOV:)

(:description Detailed modeling of wire-in-pipe communication for increased data communication rates for horizontal drilling.:)

(:keywords upstream, wire in pipe, mud pulsing, mud pulse, Slumberger, Intelliserv, NOV:)

(:description Detailed modeling of wire-in-pipe communication for increased data communication rates for horizontal drilling.:)

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''Drowning in Data, Starving for Information''

Measurement technology is advancing in the oil and gas industry. Innovations such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring have the effect of increasing the number of available measurements.

Attach:mud_pulse.jpg

This flood of information can be distilled into relevant and actionable information with Advanced Process Monitoring. The purpose of APM is to validate measurements and align imperfect mathematical models to the actual process. The objective of this approach is to determine a best estimate of the current state of the process and any potential disturbances. The opportunity is in earlier detection of disturbances, process equipment faults, and improved state estimates for optimization and control.

Measurement technology is advancing in the oil and gas industry. Innovations such as wireless transmitters, reduced cost of measurement technology, and increased regulations that require active monitoring have the effect of increasing the number of available measurements.

Attach:mud_pulse.jpg

This flood of information can be distilled into relevant and actionable information with Advanced Process Monitoring. The purpose of APM is to validate measurements and align imperfect mathematical models to the actual process. The objective of this approach is to determine a best estimate of the current state of the process and any potential disturbances. The opportunity is in earlier detection of disturbances, process equipment faults, and improved state estimates for optimization and control.

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Below is a simple example of a mathematical model of inductor-connected wire-in-pipe technology provided by Intelliserv, a JV between Slumberger and NOV.

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! The principal input to the model is the voltage to the motor.

to:

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!! Drill Shaft Communication

(:html:)<font size=2><pre>

! APMonitor Modeling Language

! https://www.apmonitor.com

! The principal input to the model is the voltage to the motor.

! Parameters include resistance (ohm), winding inductance (henrys)

Model pipe

Parameters

! communication parameters

v_in = 0 ! input voltage (Volt)

R = 0.1 ! resistance (Ohm)

L = 1e-5 ! inductance (Henry)

C = 1e-8 ! capacitance (Farad)

t[1] = 23 ! temperature in first pipe segment (°C)

t[2:10] = t[1:9] + 10 ! temperature increases by 5°C with each segment

n[1:10] = 100 ! number of windings on each pipe segment

! can modify for different number of windings on each end

End Parameters

Variables

i[1:10] = 0 ! current (Amps)

v[1:10] = 0 ! voltage (Volt)

End Variables

Intermediates

! loss across pipe connections (dB)

! linear correlation (20°C = 0.5 dB, 120°C = 1.5 dB)

dB[1:10] = (t[1:10] - 20) * (1.5-0.5)/(120-20) + 0.5

! inductor to inductor transfer efficiency

eff[1:10] = 10^(-db[1:10]/20), >=0, <=1

eff_avg[1:9] = (eff[1:9] + eff[2:10]) / 2

End Intermediates

Equations

! input voltage effect on current in 1st pipe

L*$i[1] = -R*i[1] + v_in

! current dynamics in each segment

L*$i[2:10] = -R*i[2:10] + (n[2:10]/n[1:9]) * v[1:9] * eff_avg[1:9]

! voltage dynamics from capacitance

C * $v[1:10] = i[1:10] - v[1:10]/R

End Equations

End Model

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(:html:)<font size=2><pre>

! APMonitor Modeling Language

! https://www.apmonitor.com

! The principal input to the model is the voltage to the motor.

! Parameters include resistance (ohm), winding inductance (henrys)

Model pipe

Parameters

! communication parameters

v_in = 0 ! input voltage (Volt)

R = 0.1 ! resistance (Ohm)

L = 1e-5 ! inductance (Henry)

C = 1e-8 ! capacitance (Farad)

t[1] = 23 ! temperature in first pipe segment (°C)

t[2:10] = t[1:9] + 10 ! temperature increases by 5°C with each segment

n[1:10] = 100 ! number of windings on each pipe segment

! can modify for different number of windings on each end

End Parameters

Variables

i[1:10] = 0 ! current (Amps)

v[1:10] = 0 ! voltage (Volt)

End Variables

Intermediates

! loss across pipe connections (dB)

! linear correlation (20°C = 0.5 dB, 120°C = 1.5 dB)

dB[1:10] = (t[1:10] - 20) * (1.5-0.5)/(120-20) + 0.5

! inductor to inductor transfer efficiency

eff[1:10] = 10^(-db[1:10]/20), >=0, <=1

eff_avg[1:9] = (eff[1:9] + eff[2:10]) / 2

End Intermediates

Equations

! input voltage effect on current in 1st pipe

L*$i[1] = -R*i[1] + v_in

! current dynamics in each segment

L*$i[2:10] = -R*i[2:10] + (n[2:10]/n[1:9]) * v[1:9] * eff_avg[1:9]

! voltage dynamics from capacitance

C * $v[1:10] = i[1:10] - v[1:10]/R

End Equations

End Model

</pre></font>(:htmlend:)