Nested Pipe Thickness Estimation Using Multi-Channel Induction
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Solution Overview
Problem
Existing methods for estimating the thickness and material properties of nested metal pipes in oil and gas wells are inefficient and inaccurate due to the non-linear combination of signals from multiple pipes, leading to artifacts and computational challenges, especially when eccentricity effects are present.
Innovation Solution
A method utilizing a multi-channel induction tool with a forward model that assumes centralization of pipes, followed by inversion techniques to minimize signal mismatch, and includes pre- and post-processing to refine collar picks and remove artifacts, using electromagnetic sensing to estimate magnetic permeability and conductivity, and employing parallel computing for computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-frequency multi-spacing induction measurements are used to estimate pipe thickness and material properties, then measurement precision is improved, but device complexity increases due to the need for multiple transmitters and receivers at different frequencies and spacings
Solution Approach 1:
The multi-channel induction tool is segmented into multiple independent transmitter-receiver channels, each operating at different frequencies and spacings. This segmentation allows the system to probe different depths and aspects of the nested pipe structure independently, improving measurement precision while maintaining manageable device complexity through modular architecture
Solution Approach 2:
The system extends measurements into multiple dimensions by utilizing different frequencies (affecting skin depth) and different spacings between transmitters and receivers. This multi-dimensional approach enables differentiation between signals from inner and outer pipes, improving thickness estimation accuracy for nested pipe configurations
2Manufacturing precision
If inversion techniques are used to analyze signal levels at different channels, then manufacturing precision of thickness estimation is improved, but loss of time increases due to computational intensity
Solution Approach 1:
The system performs preliminary calibration measurements and establishes reference signatures before conducting the actual thickness estimation. By pre-characterizing the system response and storing reference data, the inversion process during field operations requires less computational time while maintaining high precision in thickness and material property estimates
Solution Approach 2:
The inversion technique uses iterative feedback where estimated parameters are refined by comparing predicted signals with actual measurements. This feedback loop continues until convergence, improving estimation precision while the feedback mechanism efficiently directs computational resources toward the most critical parameters, reducing overall processing time
3Ease of operation
If forward model assuming centralization of pipes is used, then ease of operation is improved, but measurement precision deteriorates when eccentricity effects are present
Solution Approach 1:
The system introduces an intermediary calibration process that accounts for eccentricity effects. By measuring known reference structures and characterizing the deviation from centralized positioning, the system creates correction factors that mediate between the simplified centralized model and the actual eccentric pipe configuration, maintaining ease of operation while improving measurement precision
Solution Approach 2:
The system dynamically adjusts measurement parameters such as frequency and spacing based on detected eccentricity conditions. When eccentricity is detected, the system modifies which channels are used and how the data is interpreted, allowing the simplified forward model to remain effective while compensating for eccentricity effects to maintain precision
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
Accurately estimates the thickness and material properties of nested pipes with reduced computational time and improved accuracy, allowing for effective identification and correction of corrosion and other anomalies, facilitating precise well operations.
Implementation Method 1
One type of corrosion monitoring tool uses electromagnetic (EM) fields to estimate pipe thickness or other corrosion indicators
Implementation Method 2
a multi-channel induction tool may collect data on pipe thickness to produce an EM log
Data Source
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AI summary
A method and system for determining properties of a pipe string using multi-channel induction measurements. The method may comprise disposing a multi-channel induction tool in a cased hole, obtaining a multi-channel measurement, forming a log from the multi-channel measurement, extracting at least one abnormality that corresponds to known metal thickness, performing a search to find a set of pipe material properties that minimize a mismatch between the abnormality and a simulated response, and inverting the log to estimate the set of pipe material properties at one or more depth points using the set of pipe material properties. The system may comprise a multi-channel induction tool. The multi-channel induction tool may comprise at least one transmitter, at least one receiver, and an information handling system.