Method and apparatus for artificially intelligent model-based control of dynamic processes using probabilistic agents

• US 9,645,575 B2
• Filed: 11/27/2013
• Issued: 05/09/2017
• Est. Priority Date: 11/27/2013
• Status: Active Grant

• Current Assignee: Adept AI Systems, Inc.
• Original Assignee: Adept AI Systems, Inc.
• Inventors: Watson, Jeff
• Primary Examiner(s): ORTIZ RODRIGUEZ, CARLOS R
• Application Number:
  US14/092,803
• Publication Number:
  US 20150148919A1
• Time in Patent Office: 1,259 Days
• CPC Class Codes: E21B 43/126 Adaptations of down-hole pump systems powered by drives outside the borehole, e.g. by a rotary or oscillating drive powered by fluid E21B43/129
• CPC Class Codes: E21B 47/008 Monitoring of down-hole pump systems, e.g. for the detection of "pumped-off" conditions
• CPC Class Codes: E21B 47/06 Measuring temperature or pressure
• CPC Class Codes: E21B 47/07 Temperature
• CPC Class Codes: G05B 13/041 in which a variable is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 23/0297 Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A system and method for controlling a process such as an oil production process is disclosed. The system comprises multiple intelligent agents for processing data received from a plurality sensors deployed in a job site of an oil well, and applies a probabilistic model for evaluating risk and recommending appropriate control action to the process.

Claims

1. A method for controlling a process of at least one device, the method comprising:

receiving real time process data generated from the process;

processing the real time process data, and comparing the processed data against at least one reference of a process model of the process for establishing any deviation from normal operation;

classifying, at a status agent, a source of the deviation and a likely cause of the deviation;

prioritizing actuation of one or more diagnostic agents related to the causality of the deviation at a supervisory manager;

actuating the one or more diagnostic agents;

communicating the results of the diagnostic agents to the supervisory manager;

modifying, under the instruction of the supervisory manager, the process model based on the results of the one or more diagnostic agents, if the source of the deviation is the process model; and

applying, under the instruction of the supervisory manager, the process model at a control agent for evaluating risk and recommending and initiating at least one control action to control the process of the at least one device.

2. The method of claim 1 wherein the prioritizing actuation of one or more diagnostic agents further comprises:

pattern matching to perform a probabilistic match to known patterns; and

classifying which of the one or more diagnostic agents are initially activated.

3. The method of claim 1 wherein the supervisory manager initiates a model tuning agent based on the output of the diagnostic agents.

4. The method of claim 1 further comprising:

prior to initiating the at least one control action, optimizing the at least one control action.

5. The method of claim 1 wherein the one or more diagnostic agents are sub-agents of an encompassing diagnostic agent.

6. The method of claim 1 wherein each of the agents can operate individually as separate agents, or as a sub-agent of an encompassing agent.

7. The method of claim 6 wherein the encompassing agent comprises an object-oriented Bayesian Network.

17. The method of claim 1 further comprising, after modifying the process model:

re-processing the real time process data, and comparing the re-processed data against at least one reference of the modified process model for establishing any deviation from normal operation;

classifying, at a status agent, a source of the deviation and a likely cause of the deviation;

prioritizing actuation of one or more additional diagnostic agents related to the causality of the deviation at the supervisory manager;

actuating the one or more additional diagnostic agents;

communicating the results of the additional diagnostic agents to the supervisory manager; and

modifying, under the instruction of the supervisory manager, the process model based on the results of the one or more additional diagnostic agents, if the source of the deviation is the process model.

18. The method of claim 1 further comprising:

prioritizing actuation of one or more additional diagnostic agents if sufficiently uncertain to differentiate a likely cause of the deviation.

19. The method of claim 1 wherein said classifying a source and likely cause of the deviation comprises:

applying probabilistic modeling at the status agent for classifying a source and likely cause of the deviation.

20. The method of claim 3 wherein the model tuning agent initiates and prioritizes numerical or iterative methods with the process model to identify and quantify the possible contributions to the deviations.

21. The method of claim 1 wherein the at least one reference of the process model comprises at least one of a predictive objective function and/or a historical mean and/or a predicted operational envelope and/or a predetermined static function.

22. The method of claim 1 wherein at least one agent employs a Probabilistic Graphical Network.

23. The method of claim 1 wherein at least one agent employs a Bayesian Network.

8. A method of controlling a process comprising:

comparing real time data to at least one reference of a process model of the process, said comparing taking into account operational variance and thereby identifying deviations;

tracking the process and the specific states thereof;

monitoring and identifying variations of the process from normal operation, and generating outputs;

based on said outputs, classifying one or more diagnoses relevant to the deviations;

prioritizing the classified diagnoses for identifying probable causes for the deviation and the likelihood of each of the probable causes;

comparing the diagnoses with the process model and updating the process model based on a diagnosis of high certainty if the cause for the deviation is the process model, and if sufficiently uncertain to differentiate a likely cause, initiating additional diagnoses;

assessing opportunity and/or risk of control action based on the identified probable causes for the deviation and their likelihood; and

recommending at least one control action to control the process based on said assessment.

24. The method of claim 8 wherein the at least one reference of the process model of the process comprises at least one of a static model of the process, a statistically generated mean reference, a model-based predictive reference, statistical and/or historical profiles of the process and a simulated profile of the process.

25. The method of claim 8 wherein said monitoring and identifying variations of the process from normal operation comprises:

monitoring and identifying variations of the process from normal operation using probabilistic modeling.

26. The method of claim 25 wherein said probabilistic modeling comprises at least one Probabilistic Graphical Model based state model.

27. The method of claim 8 further comprising:

after updating the process model, repeating the monitoring, classifying, prioritizing and comparing steps.

9. A system for controlling a process of at least one device in response to data collected from a plurality of sensors, said system comprising:

a status agent for monitoring the status of the system based on data collected from the sensors;

at least one diagnostic agent for diagnosing at least a portion of the system and collecting diagnostic data from the sensors to identify deviation from normal operation;

a control agent for performing control actions to the process; and

a supervisory manager coupling to the status agent, the at least one diagnostic agent and the control agent for;

receiving real time process data generated from the process;

comparing against at least one of a predictive objective function, a historical mean, a predicted operational envelope and a predetermined static function of a process model of the process, for establishing any deviation from normal operation;

classifying, at the status agent, a source of the deviation and likely cause of the deviation;

prioritizing actuation of the at least one diagnostic agent related to causality of the deviation;

modifying the process model based on the results of the at least one diagnostic agent, if the source of the deviation is the process model; and

applying the process model at a control agent for evaluating risk and recommending appropriate control action to control the process of the at least one device.

10. The system of claim 9 wherein the supervisory manager further couples to the status agent, the at least one diagnostic agent and the control agent for:

initiating at least one control action to the process.

11. The system of claim 9 wherein each of the agents can operate individually as a separate agent, or as a sub-agent of an encompassing agent.

12. The system of claim 11 wherein the encompassing agent comprises an object-oriented Bayesian Network or an undirected probabilistic network.

13. The system of claim 9 wherein the at least one device comprises at least one pumpjack.

14. The system of claim 9 wherein the at least one device comprises at least one of steam methane reformer, boiler, heat treatment furnace, water treatment equipment, and compressor skid.

15. The system of claim 9 wherein the system is distributed in a plurality of jobsites for controlling the devices thereon.

16. The system of claim 9 wherein said classifying a source and likely cause of the deviation comprises:

applying probabilistic modeling at the status agent for classifying a source and likely cause of the deviation.