System and methods for the measurement of drilling mud flow in real-time

Patent metadata

Field	Value
Publication number	US-11867008-B2
Application number	US-202017090106-A
Country	US
Kind code	B2
Filing date	Nov 5, 2020
Priority date	Nov 5, 2020
Publication date	Jan 9, 2024
Grant date	Jan 9, 2024

Abstract

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for monitoring the flow of drilling mud from a wellbore by image processing. One method includes: capturing, using a digital imaging device, images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system that circulates drilling mud through the wellbore and a wellbore drilling assembly; receiving, by one or more processors of a computer system operatively coupled to the digital imaging device, the images captured by the digital imaging device; and processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones.

First claim

What is claimed is:

1. A method for monitoring the flow of drilling mud from a wellbore, comprising:
capturing, using a digital imaging device, images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system that circulates drilling mud through the wellbore and a wellbore drilling assembly;
receiving, by one or more processors of a computer system operatively coupled to the digital imaging device, the images captured by the digital imaging device; and
processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones, wherein processing the images comprises:
deploying a machine learning model to extract abstract features from the images captured by the digital imaging device, wherein the machine learning model comprises a convolutional neural network (CNN) model, and
determining the rate of flow of the drilling mud through the one or more image capture zones by processing, using the CNN, the abstract features and additional features that comprise one or more of drilling parameters of the wellbore drilling assembly, properties of the drilling mud, and the weight of drilling mud measured in the one or more image capture zones using one or more mass sensors.

2. The method of claim 1 , further comprising:
receiving a mud flow-in rate from a flow rate sensor of the circulation system;
calculating a delta flow based on a difference between the mud flow-in rate and the rate of flow of the drilling mud through the one or more image capture zones; and
displaying, using a display device of the computer system, a mud flow status based on the delta flow.

3. The method of claim 2 , further comprising:
identifying one or more recommended actions for operating the wellbore drilling assembly based on the delta flow or the mud flow status; and
displaying the one or more recommended actions on the display device.

4. The method of claim 2 , wherein the one or more image capture zones comprise a discharge zone where a flow-out line connects to a possum belly, the possum belly being configured to receive drilling mud from the flow-out line.

5. The method of claim 4 , wherein the circulation system comprises one or more shaker assemblies, each comprising a splash zone configured to receive drilling mud from the possum belly, wherein the one or more image capture zones comprise the splash zone of each of the one or more shaker assemblies.

6. The method of claim 5 , wherein the one or more shaker assemblies comprises a plurality of shaker assemblies, wherein the method further comprises:
determining a partial flow rate of drilling mud through the splash zone of each of the plurality of shaker assemblies; and
combining the partial flow rates to obtain the rate of flow of the drilling mud.

7. The method of claim 1 , wherein determining the rate of flow of the drilling mud through the one or more image capture zones by processing, using the CNN comprises:
receiving the additional features;
concatenating the abstract features extracted by the CNN and the additional features; and
feeding the concatenated features as input to a regression model to determine the rate of flow of the drilling mud through the one or more image capture zones.

8. A system for monitoring the flow of drilling mud from a wellbore, comprising:
a digital imaging device configured to capture images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system configured to circulate drilling mud through the wellbore and a wellbore drilling assembly;
a computer system operatively coupled to the digital imaging device, the computer system comprising one or more processors and a computer-readable medium storing instructions executable by the one or more processors to perform operations comprising:
receiving the images captured by the digital imaging device; and
processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones, wherein processing the images comprises deploying a convolutional neural network (CNN) model that determines the rate of flow of the drilling mud through the one or more image capture zones using abstract features extracted from the images captured by the digital imaging device and additional features that comprise one or more of drilling parameters of the wellbore drilling assembly, properties of the drilling mud, and the weight of drilling mud measured in the one or more image capture zones.

9. The system of claim 8 , further comprising a flow rate sensor configured to measure a mud flow-in rate of the drilling mud through the wellbore drilling assembly and into the wellbore,
wherein the computer system comprises a display device and is configured to perform operations comprising:
receiving the mud flow-in rate from the flow rate sensor;
calculating a delta flow based on a difference between the mud flow-in rate and the rate of flow of the drilling mud through the one or more image capture zones;
identifying a mud flow status based on the delta flow;
identifying one or more recommended actions for operating the wellbore drilling assembly based on the delta flow or the mud flow status; and
displaying the mud flow status and the one or more recommended actions on the display device.

10. The system of claim 8 , further comprising a possum belly configured to receive drilling mud from a flow-out line of the circulation system, wherein the one or more image capture zones comprise a discharge zone where the flow-out line connects to the possum belly.

11. The system of claim 10 , wherein the digital imaging device comprises a camera mounted to or adjacent to the possum belly and oriented to face the discharge zone of the possum belly.

12. The system of claim 10 , further comprising one or more shaker assemblies, wherein each of the one or more shaker assemblies comprises a splash zone configured to receive drilling mud from the possum belly, wherein the one or more image capture zones comprise the splash zone of each of the one or more shaker assemblies.

13. The system of claim 12 , wherein each of the one or more shaker assemblies comprises a flow pane with an adjustable opening through which drilling mud flows from the possum belly into the splash zone, wherein the one or more image capture zones comprise the flow pane of each of the one or more shaker assemblies.

14. The system of claim 13 , wherein the digital imaging device comprises a camera mounted to or adjacent to each of the one or more shaker assemblies, wherein each camera is oriented to face the splash zone and flow pane of the one or more shaker assemblies.

15. The system of claim 12 , wherein the digital imaging device comprises a camera mounted above and oriented to face the possum belly and the one or more shaker assemblies, wherein the camera comprises a field of view that comprises the discharge zone of the possum belly and the splash zone of each of the one or more shaker assemblies.

16. The system of claim 8 , wherein deploying the CNN model comprises:
deploying the CNN model to extract the abstract features from the images captured by the digital imaging device;
receiving the additional features that comprise the one or more of drilling parameters of the wellbore drilling assembly, the properties of the drilling mud, and the weight of drilling mud measured

Assignees

• Saudi Arabian Oil Co

Inventors

• Mora Arturo Magana
• Gooneratne Chinthaka Pasan
• Affleck Michael
• Contreras Otalvora William

Classifications

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• 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

• E21B21/065 Primary

  Separating solids from drilling fluids · CPC title