Auto-Spooling Control Using Stochastic Inference for Fleet Angle Stability
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Solution Overview
Problem
Hydrocarbon recovery operations involving wireline, coiled tubing, and slickline face challenges such as entanglement, dropped loads, and increased costs due to manual and inefficient processes, necessitating the development of automated spooling systems to enhance operational safety and reduce costs.
Innovation Solution
An automated spooling system comprising a sensor and measurement system, data-driven system identification, stochastic inference, and an auto-spooling controller that monitors and controls the spooling process, maintaining the fleet angle within a specific range to prevent damage and improve the service life of elongated members, using a combination of sensors, computer vision, and Bayesian estimation for real-time state estimation and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated spooling control system is implemented, then operational efficiency and reliability are improved, but device complexity increases
Solution Approach 1:
The system continuously monitors spooling parameters (drum position, elongated member length, spooling pressure, tension) and uses stochastic inference to estimate fleet angle and layer direction, providing real-time feedback to the auto-spooling controller to maintain optimal spooling conditions and prevent entanglement
Solution Approach 2:
The patent replaces manual mechanical spooling control with an automated system using sensors, data-driven system identification, stochastic inference algorithms, and computer vision to estimate spool state and control spooling parameters, reducing human intervention while improving reliability
2Device complexity
If manual spooling operations are performed to reduce costs, then device complexity is reduced, but operational reliability and safety deteriorate
Solution Approach 1:
The system performs self-monitoring and self-correction by automatically detecting spooling anomalies through sensors and computer vision, estimating fleet angle and layer direction, and adjusting spooling parameters without human intervention to maintain optimal operation and prevent entanglement
Solution Approach 2:
The patent introduces an auto-spooling controller as an intermediary between the mechanical spooling system and the operator, using stochastic inference and data-driven system identification to bridge the gap between simple mechanical operation and intelligent control, improving reliability while maintaining ease of operation
3Productivity
If rushed operations are performed to reduce costs, then productivity is improved, but operational safety and reliability deteriorate
Solution Approach 1:
The system performs preliminary detection and estimation of spooling parameters (fleet angle, layer direction) before critical failures occur, using sensors and stochastic inference to predict potential entanglement or dropped loads, allowing preventive action to be taken before safety is compromised
Solution Approach 2:
The patent implements dynamic adjustment of spooling parameters based on real-time conditions, allowing the system to optimize spooling speed and tension dynamically rather than using fixed rigid control, enabling high productivity while maintaining safety through adaptive response to changing conditions
Data Source
AI summary
A system and method that is configured to automatically control a spooling of a drum of a deployment unit used in hydrocarbon recovery operations. The method may comprise obtaining data related to a sensor and measurements system connected to the deployment unit. Processing the data obtained by the sensor and measurements system in a processing unit that makes a stochastic inference. Controlling an auto spooling of the drum of the deployment unit through an auto-spooling controller connected to the deployment unit based upon the stochastic inference.