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

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.

The embodiments of the invention for which an exclusive property or privilege is claimed are defined as follows: 
     
         1 . A method for controlling a process comprising:
 receiving real time process data generated from the process;   comparing against a predictive objective function and/or a historical mean and/or a predicted operational envelope and/or a predetermined static function for establishing any deviation from normal operation;   applying probabilistic modeling at a status agent for classifying the source and likely cause of the deviation;   prioritizing actuation of at least one or more diagnostic agents related to causality of the deviation at a supervisory manager;   communicating the results of the diagnostic agents to the supervisory manager;   modifying the probabilistic model of the status agent from the results from the one or more diagnostic agents; and   applying a probabilistic model at a control agent for evaluating risk and recommending and initiating an appropriate control action to the process.   
     
     
         2 . A method of controlling a process comprising:
 comparing real time data to statistical and/or historical profiles of the process or a simulated profile of the process, taking into account operational variance and thereby identifying deviations;   tracking the progress and the specific states of the process at a status agent and classifying probable diagnostic agents relevant to the deviations;   and under the management of an AI Manager,
 triggering the appropriate diagnostic agents with causal process-specific PGNs for identifying probable causes for the deviation and their likelihood, and receiving the probabilistic diagnoses; 
 comparing the probabilistic diagnoses of the various diagnostic agents and updating the Status Agent of the diagnosis of high certainty, or if sufficiently uncertain to differentiate a likely cause, initiating additional tests; and 
 assessing opportunity and/or risk of control action at a control agent based on the probability of one or more diagnoses and recommending a control action to the process based on the opportunity/risk analysis. 
   
     
     
         3 . A system for controlling a process 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 a predictive objective function and/or a historical mean and/or a predicted operational envelope and/or a predetermined static function for establishing any deviation from normal operation;
 applying probabilistic modeling at the status agent for classifying the source and likely cause of the deviation; 
 prioritizing actuation of the at least one diagnostic agent related to causality of the deviation; 
 modifying the probabilistic model of the status agent from the results from the at least one diagnostic agent; and 
 applying a probabilistic model at a control agent for evaluating risk and recommending appropriate control action to the process.