Electromagnetic Pipe Corrosion Detection Using Perturbation Inversion
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Solution Overview
Problem
Traditional electromagnetic inversion techniques for detecting corrosion in downhole metal pipes are slow and computationally expensive, especially when dealing with multiple concentric pipes, due to the need for repeated simulations using a forward model.
Innovation Solution
The proposed method employs a perturbation technique to improve the efficiency of electromagnetic defect detection by using linear approximations and interpolations, reducing the need for full forward model calculations, and combining this with regular inversion for enhanced accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electromagnetic inversion techniques are used for corrosion detection in multiple concentric pipes, then measurement precision is improved, but productivity deteriorates due to slow and computationally expensive repeated simulations
Solution Approach 1:
The patent pre-calculates and stores sensitivity matrices and response data for various pipe thickness scenarios before actual corrosion detection. This preliminary computation allows the inversion process to use pre-stored data and linear approximations rather than performing full forward model simulations during actual detection, dramatically speeding up processing while maintaining accuracy
Solution Approach 2:
The patent transforms the traditional nonlinear inversion problem into a linear approximation problem by changing the mathematical parameters and approach. Instead of repeatedly solving the full electromagnetic forward model, the method uses pre-computed sensitivity matrices and linear relationships between pipe properties and EM responses, reducing computational complexity while preserving measurement precision
2Measurement precision
If full forward model calculations are performed repeatedly during inversion, then measurement precision is improved, but loss of time increases due to computational expense
Solution Approach 1:
The patent performs and stores all time-consuming forward model calculations in advance, creating lookup tables and pre-computed sensitivity matrices for various pipe configurations. During actual corrosion detection, the system only needs to query these pre-computed data structures and perform simple linear algebra operations, eliminating repeated full forward model simulations and reducing computation time significantly
Solution Approach 2:
The patent creates simplified copies or approximations of the full forward model by using pre-computed sensitivity matrices and linear relationships. These mathematical copies allow the inversion process to estimate pipe thickness accurately without invoking the computationally intensive full electromagnetic forward model repeatedly
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 approach significantly speeds up the inversion process by hundreds without major loss in accuracy, providing cost savings and enabling more efficient corrosion detection in complex pipe configurations.
Implementation Method 1
One type of corrosion detection tool uses electromagnetic (EM) fields to estimate pipe thickness or other corrosion indicators. As an example, an EM logging tool may collect EM log data, where the EM log data may be interpreted to correlate a level of flux leakage or EM induction with corrosion.
Data Source
AI summary
Systems and methods for detection of pipe characteristics, such as defect detection of downhole tubulars and overall thickness estimation of downhole tubulars (e.g., pipes such as casing and/or production tubing). A defect detection method may comprise disposing a defect detection tool in a wellbore, wherein the defect detection tool comprises at least one transmitter and at least one receiver; obtaining nominal parameters of pipe properties; determining nominal responses corresponding to the nominal parameters; determining a defect profile for a plurality of pipes disposed in a wellbore; determining defected responses for the defection detection tool from at least the nominal parameters and the defect profile; calculating a gradient from at least the defected responses, the nominal responses, the nominal parameters and the defect profile; making downhole measurements of the plurality of pipes using the defect detection tool; and calculating final solution parameters of the plurality of pipes using at least the downhole measurements, the nominal responses, the gradient and the nominal parameters.