Threaded Tubular Connection Evaluation Using Make-Up Graph Models
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Solution Overview
Problem
Current methods for evaluating the connection quality of threaded tubular components in oil and gas production are unreliable and prone to human error, leading to potential sealing defects and safety risks, as they depend on operator skill and interpretation of torque and revolution data.
Innovation Solution
A method combining a first model that evaluates connection quality based on primary numeric variables and a second model driven by machine learning, using elementary variables from reference make-up graphs to accurately assess the conformity of tubular connections, reducing the risk of incorrect evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated machine learning methods are used to evaluate connection quality, then productivity and reliability are improved, but measurement precision may be reduced due to algorithmic limitations
Solution Approach 1:
The patent combines expert-based evaluation criteria with machine learning algorithms into a hybrid system. The expert criteria provide interpretable rules for assessing make-up graphs, while the machine learning model automates pattern recognition. This merging allows the system to maintain high reliability through expert knowledge while achieving automation benefits, resolving the contradiction between manual precision and automated efficiency.
2Measurement precision
If manual evaluation by competent persons is used, then measurement precision is maintained through expert interpretation, but productivity decreases and human safety risks increase
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an automated computer-based system. The system uses software algorithms to analyze make-up graphs, torque data, and revolution counts, substituting human operators with automated computational methods. This substitution maintains measurement precision through consistent algorithmic application while dramatically improving productivity and eliminating the need for human presence during evaluation.
3Ease of operation
If simple evaluation criteria are used, then ease of operation is improved, but measurement precision deteriorates due to inability to detect subtle defects
Solution Approach 1:
The patent segments the evaluation process into multiple distinct criteria: torque analysis, revolution count analysis, make-up graph shape evaluation, and conformity checks against predefined thresholds. Each segment focuses on a specific aspect of connection quality, making the overall complex evaluation manageable through modular components. This segmentation maintains ease of operation by breaking down complexity into discrete, programmable steps while preserving measurement precision through comprehensive multi-factor analysis.
Data Source
AI summary
A method for connecting threaded portions of a first tubular component and a second tubular component, including the obtaining of a make-up graph. The method further includes the evaluation of the connection quality of the first and second tubular components, on the basis of first and second models, by acceptance or rejection of the make-up graph obtained and assignment of a connection status respectively representing the conforming or non-conforming state of the connection of the first and second tubular components.