Autonomous Downhole Shifting Tool with Neural Network Control
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Solution Overview
Problem
Current wireline shifting tools for downhole sliding sleeves in the oil and gas industry require extensive human operator training and are prone to errors due to complex operations, increasing costs and operational risks.
Innovation Solution
A controller system integrated with the shifting tool that autonomously locates and repositions downhole sliding sleeves using a combination of linear actuators, anchoring systems, and machine learning algorithms to reduce reliance on human operators, enabling precise and repeatable operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireline shifting tools are operated manually with human operators, then operational control and decision-making are possible, but extensive training is required and human error increases
Solution Approach 1:
The shifting tool is equipped with autonomous control systems including sensors, processors, and actuators that enable the tool to automatically locate, engage, and shift sliding sleeves without human intervention. The tool performs self-diagnosis and self-adjustment during operations, eliminating the need for extensively trained operators and reducing human error while maintaining operational reliability.
Solution Approach 2:
Manual mechanical operations are replaced with automated electro-mechanical systems. The patent incorporates electronic sensors, microprocessors, and automated actuators that substitute human manual control, thereby reducing operational complexity and training requirements while improving reliability through consistent automated execution of shifting operations.
2Productivity
If autonomous control systems are implemented in shifting tools, then human error is reduced and operational efficiency improves, but device complexity and initial costs increase
Solution Approach 1:
The autonomous control system is divided into modular functional segments including separate modules for localization, engagement detection, actuation control, and data processing. Each module performs a specific function and can be independently tested and maintained, which manages system complexity while enabling high operational efficiency through coordinated automated operations.
Solution Approach 2:
The control system is designed with multi-functional capabilities that allow a single integrated system to perform multiple operations: locating sliding sleeves, determining their status, engaging with them, and executing shifts. This universal approach consolidates what could be multiple separate systems into one unified autonomous controller, improving productivity without proportionally increasing complexity.
3Measurement precision
If precise autonomous control is used for locating and repositioning sliding sleeves, then operational precision improves, but the complexity of control systems and algorithms increases
Solution Approach 1:
The system incorporates continuous feedback loops where sensors monitor the position of the shifting tool relative to sliding sleeves, provide real-time data to the control processor, and enable automatic adjustments. This feedback mechanism achieves high location precision through iterative correction without requiring overly complex control algorithms, as the system naturally converges on the target position through sensor-guided adjustments.
Solution Approach 2:
The autonomous system performs preliminary actions including pre-localization of sliding sleeves using sensors before engagement, and pre-positioning of shifting components before actual shifting operations. By preparing and positioning elements in advance with precision, the system reduces the complexity of real-time control during critical operations, as much of the precision work is completed during the preliminary phases.
Data Source
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AI summary
Apparatus and methods for autonomously shifting a downhole sliding sleeve. A shift tool includes a shifter arm, an artificial neural network, and a control circuit. The artificial neural network is trained to identify engagement of the shifter arm with a shifting feature of a sliding sleeve. The control circuit is configured to extend the shifter arm at a first pressure for seeking engagement with the shifting feature of the sliding sleeve, and responsive to the artificial neural network recognizing engagement of the shifter arm with the shifting feature of the sliding sleeve, extend the shifter arm at a second pressure for shifting the sliding sleeve.