Controlling surface pressure during well intervention

Patent metadata

Field	Value
Publication number	US-12012811-B1
Application number	US-202218082682-A
Country	US
Kind code	B1
Filing date	Dec 16, 2022
Priority date	Dec 16, 2022
Publication date	Jun 18, 2024
Grant date	Jun 18, 2024

Abstract

A system for controlling pressure applied in a well intervention operation using a physics-based model is provided. The system can include a stripper element that includes a pressure retention element for sealing a wellbore during an intervention operation that uses coiled tubing; a stripper circuit that includes a hydraulic actuator to apply a pressure to the pressure retention element; a processing device coupled to the hydraulic actuator that can receive, from the stripper circuit, a feedback signal. The processing device may receive a physical characteristic of a component and then determine, using data from the feedback signal and the physical characteristic, a minimum pressure level to contain wellhead pressure. The processing device may then output a command to cause the hydraulic actuator to change the pressure on the pressure retention element to be the minimum pressure level or within a pre-set deviation of the minimum pressure level.

First claim

What is claimed is:

1. A system comprising:
a stripper element that includes a pressure retention element for sealing a wellbore during an intervention operation that uses coiled tubing;
a stripper circuit that includes a hydraulic actuator to apply a pressure to the pressure retention element;
a processing device communicatively coupled to the hydraulic actuator; and
a memory device including instructions that are executable by the processing device for causing the processing device to:
receive, from the stripper circuit, a feedback signal;
receive a plurality of physical characteristics of one or more components used during the intervention operation;
determine, using the feedback signal and the plurality of physical characteristics input to a physics-based model, a minimum pressure level to contain wellhead pressure and a maximum pressure level configured to prevent damage to the coiled tubing; and
output a command to cause the hydraulic actuator to change the pressure on the pressure retention element to maintain the pressure between the minimum pressure level and the maximum pressure level.

2. The system of claim 1 , further comprising a surface sensor, wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
receive, from the surface sensor, a surface sensor output;
input, to a pressure control model comprising the physics-based model, the surface sensor output; and
update, using the pressure control model, the feedback signal, and the surface sensor output, the minimum pressure level to contain wellhead pressure.

3. The system of claim 2 , further comprising an overpressure backup component that includes an overpressure actuator, wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
receive, from a leak sensor, a leak sensor output;
input, to the pressure control model, the leak sensor output;
determine, via the pressure control model and using the leak sensor output, a wellhead overpressure condition; and
output first commands to cause the overpressure actuator to contain the wellhead pressure.

4. The system of claim 3 , wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
identify, using the pressure control model and the leak sensor output, a wellhead leak;
quantify, using the pressure control model and the leak sensor output, a magnitude of the wellhead leak;
classify, using the pressure control model and the leak sensor output, an event classification corresponding to a severity of the wellhead leak; and
output second commands to cause the hydraulic actuator and the overpressure actuator to respond to the wellhead leak with an action that is based on the severity of the wellhead leak.

5. The system of claim 2 , wherein the pressure control model comprises a learning module, wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
input, to the learning module, the feedback signal;
generate, by the learning module, a statistical learning model trained using the feedback signal, the statistical learning model trained to contain wellhead pressure; and
input, to the pressure control model, the statistical learning model.

6. The system of claim 5 , wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
access, from a memory, historical data;
input, to the statistical learning model, the surface sensor output and the historical data; and
generate, by the learning module, the statistical learning model trained using the feedback signal, the surface sensor output, and the historical data, the statistical learning model trained to contain wellhead pressure.

7. The system of claim 2 , further comprising a lubrication system configured to reduce friction between the pressure retention element and the coiled tubing, including a lubrication system actuator, wherein the memory device further includes instructions executable by the processing device for causing the processing device to:
identify, using the pressure control model and the feedback signal, a lubrication deficit; and
output commands to cause the lubrication system actuator to reduce the lubrication deficit.

8. A method comprising:
receiving, from a stripper circuit that includes a hydraulic actuator to apply a pressure to a pressure retention element, a feedback signal, wherein the pressure retention element is included in a stripper element and is used for sealing a wellbore during an intervention operation that uses coiled tubing;
receiving a plurality of physical characteristics of one or more components used in the intervention operation;
determining, using the feedback signal and the plurality of physical characteristics of the one or more components input to a physics-based model, a minimum pressure level to contain wellhead pressure and a maximum pressure level configured to prevent damage to the coiled tubing; and
outputting a command to cause the hydraulic actuator to change the pressure on the pressure retention element to maintain the pressure between the minimum pressure level and the maximum pressure level.

9. The method of claim 8 , further comprising:
receiving, from a surface sensor, a surface sensor output;
inputting, to a pressure control model comprising the physics-based model, the surface sensor output; and
updating, using the pressure control model, the feedback signal, and the surface sensor output, the minimum pressure level to contain wellhead pressure.

10. The method of claim 9 , further comprising:
receiving, from a leak sensor, a leak sensor output;
inputting, to the pressure control model, the leak sensor output;
determining, via the pressure control model and using the leak sensor output, a wellhead overpressure condition; and
outputting first commands to cause an overpressure actuator to contain the wellhead pressure, wherein the overpressure actuator is included in an overpressure backup component.

11. The method of claim 10 , further comprising:
identifying, using the pressure control model and the leak sensor output, a wellhead leak;
quantifying, using the pressure control model and the leak sensor output, a magnitude of the wellhead leak;
classifying, using the pressure control model and the leak sensor output, an event classification corresponding to a severity of the wellhead leak; and
outputting second commands to cause the hydraulic actuator and the overpressure actuator to respond to the wellhead leak with an action that is based on the severity of the wellhead leak.

12. The method of claim 9 , further comprising:
inputting, to a learning module included in the pressure control model, the feedback signal;
generating, by the learning module, a statistical learning model trained using the feedback signal, the statistical learning model trained to contain wellhead pressure; and
inputting, to the pressure control model, the statistical learning model.

13. The method of claim 12 , further comprising:
accessing, from a memory, historical data;
inputting, to the statistical learning model, the surface sensor output and the historical data; and
generating, by the learning module, the statistical learning model trained using the feedback signal, the surface sensor output, and the historical data, the statistical learning model trained to contain wellhead pressure.

14. The method of claim 9 , further com

Assignees

• Halliburton Energy Services Inc

Inventors

• Jacob Roshan
• Ogundare Oluwatosin
• Quero Philippe
• Mouser Charles Lynn
• Nicholson Jeremy C
• Rolovic Radovan
• Jantz Eric Lynn
• Domann Robert Eugene

Classifications

• E21B21/08 Primary

  Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure (valve arrangements therefor E21B21/10) · CPC title

• E21B19/22 Primary

  Handling reeled pipe or rod units, e.g. flexible drilling pipes {(lifting or hauling appliances using two or more cooperating endless chains B66D3/003)} · CPC title

• E21B33/08

  Wipers; Oil savers · CPC title

• E21B33/06

  Blow-out preventers {, i.e. apparatus closing around a drill pipe, e.g. annular blow-out preventers (rotating blow-out preventers E21B33/085)} · CPC title

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title