Abstract: An autonomous robot performs maintenance and repair activities in oil and gas wells, and in pipelines. The robot uses the well and pipeline fluids to provide most of the energy required for its mobility, and to charge a turbine from which it may recharge batteries or power a motor for additional propulsion. A control system of associated systems controls the robot, enabling the robot to share plans and goals, situations, and solution processes with wells, pipelines, and external control systems. The control system includes decision aids in a series of knowledge bases that contain the expertise in appropriate fields to provide intelligent behavior to the control system. The intelligent behavior of the control system enables real time maintenance management of wells and pipelines, through actively collecting information, goal-driven system, reasoning at multiple levels, context sensitive communication, activity performing, and estimation of the operators' intent.

Abstract: A system and method for monitoring processes in the production of oil and gas uses intelligent software agents employing associative memory techniques that receive data from sensors in the production environment and from other sources and perform pattern matching operations to identify normal and abnormal behavior of the well production. The agents report the behaviors to human operators or other software systems. The abnormal behavior may consist of any behavior of the production processes that is other than the desired behavior of the well. The intelligent software agents are trained to identify both specific behaviors and behaviors that have never before been observed and recognized in the well.

Abstract: A method for determining and reporting a general production state of a gas-lift system for an oil well, the oil well having associated tubing, casing, and gas-lift valves, and wherein sensor signals from the welt and its associated tubing and casing are input into mathematical models. The method comprises the steps of: a) extracting values from the mathematical models that indicate instantaneous states of production; b) supplying the sensor signals and the values to an associative memory agent; and c) using the associative memory agent to associate the sensor signals and the values to generate the general production state.

Abstract: An autonomous robot performs maintenance and repair activities in oil and gas wells, and in pipelines. The robot uses the well and pipeline fluids to provide most of the energy required for the its mobility, and to charge a turbine from which it may recharge batteries or power a motor for additional propulsion. A control system of associated systems controls the robot, enabling the robot to share plans and goals, situations, and solution processes with wells, pipelines, and external control systems. The control system includes decision aids in a series of knowledge bases that contain the expertise in appropriate fields to provide intelligent behavior to the control system. The intelligent behavior of the control system enables real time maintenance management of wells and pipelines, through actively collecting information, goal-driven system, reasoning at multiple levels, context sensitive communication, activity performing, and estimation of the operators' intent.

Abstract: A method of managing multiple wells connected to a reservoir comprises the steps of: setting production and performance goals for each individual well; monitoring the performance of each individual well; enabling each individual well to assess its own situational state; and enabling socially interactive corrective actions, comprising enabling collaboration and cooperation among the wells, such that the wells share with each other their situational states, goals, and plan data; developing and refining remediation strategies for problems detected within the wells; allowing execution of the remediation strategies either independently, or by operator intervention; applying the remediation strategies to each individual well; revising and resetting the goals, and integrating pattern recognition and machine learning within all preceding steps.

Abstract: A method of managing multiple wells connected to a reservoir comprises the steps of: setting production and performance goals for each individual well; monitoring the performance of each individual well; enabling each individual well to assess its own situational state; and enabling socially interactive corrective actions, comprising enabling collaboration and cooperation among the wells, such that the wells share with each other their situational states, goals, and plan data; developing and refining remediation strategies for problems detected within the wells; allowing execution of the remediation strategies either independently, or by operator intervention; applying the remediation strategies to each individual well; revising and resetting the goals, and integrating pattern recognition and machine learning within all preceding steps.