Wellhead Tubular Grip Coefficient Testing Under Ridged Contact
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Solution Overview
Problem
Existing wellhead systems face challenges in determining the grip coefficient at the interface between inner and outer tubular members, which is crucial for load capacity and reliability, as the surfaces are not ideal smooth surfaces and are influenced by material, surface treatments, and geometry, making full-scale testing costly and unsafe.
Innovation Solution
A method and apparatus are developed to determine the grip coefficient by using a test block with a ridged profile, gripped between plates, applying a gradually increasing load, and monitoring relative movement to calculate the grip coefficient through the ratio of applied load to gripping force, replicating the interface conditions of wellhead systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional friction measurement methods are used, then measurement can be performed with simple equipment, but the measurement precision is insufficient for automated gripping systems
Solution Approach 1:
The patent replaces conventional mechanical friction measurement methods with a hybrid approach combining force sensors (mechanical) and machine learning algorithms (computational). Force sensors measure normal and friction forces, while machine learning models process these measurements to determine friction coefficients, achieving higher precision than traditional mechanical methods alone.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw force sensor measurements and friction coefficient determination. This intermediary layer processes complex force measurement data to extract accurate friction coefficients, bridging the gap between simple mechanical measurements and precise friction characterization.
2Adaptability or versatility
If existing friction measurement approaches are applied, then the process is simple, but adaptability to different materials and gripping conditions is poor
Solution Approach 1:
The patent implements dynamic adaptability through machine learning models that can be trained on diverse datasets representing different materials and gripping conditions. The system transitions from static, material-specific friction values to dynamic, condition-adaptive friction coefficients that automatically adjust based on the specific gripping scenario.
Solution Approach 2:
The patent changes the approach from using fixed friction coefficients to dynamically determined friction coefficients based on multiple force measurement parameters. By measuring both normal and friction forces simultaneously and using machine learning to process these parameters, the system adapts to different materials and conditions without requiring manual reconfiguration.
3Extent of automation
If traditional friction measurement methods are used, then the system is simple to operate, but automation capability is limited
Solution Approach 1:
The patent enables self-service automation through machine learning models that automatically determine friction coefficients from force sensor measurements without requiring manual intervention. The system performs automated gripping simulations, processes measurements, and outputs friction coefficients autonomously, eliminating the need for operator expertise in friction measurement techniques.
Solution Approach 2:
The patent implements feedback loops where force sensor measurements are continuously fed into machine learning models, which then output friction coefficients that can be used to adjust gripping forces. This closed-loop feedback enables automated adaptation to different materials and conditions, enhancing automation capability while managing system complexity through algorithmic control.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for the empirical determination of grip coefficient with high statistical reliability, enabling the design and construction of wellhead systems with optimized load capacity and secure clamping, reducing the need for costly full-scale testing.
Implementation Method 1
a grip coefficient which represents a relationship between a gripping force and a contact force is determined
Data Source
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AI summary
This invention relates to a method of determining a grip coefficient in a wellhead system. The method comprises: forming a test block having opposite side surfaces including a ridged profile, the test block being made from the same material as at least an outer surface of an inner tubular member of a wellhead system; gripping the test block between two gripping plates so as to form an interface between each of the side surfaces and a test face of a respective one of the gripping plates, the test face being made from the same material as at least an inner surface of an outer tubular member of a wellhead system; applying a gradually increasing load to the test block in a direction parallel to a plane of the interface; continuously monitoring relative movement between the test block and the gripping plate at the interfaces; determining the applied load at which slip between the test block and the gripping plates occurs; and determining the grip coefficient by calculating the ratio of the applied load to the gripping force at the time slip occurs.