Adaptive EM Pipe Inspection for Multi-String Corrosion Detection
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Solution Overview
Problem
In oil and gas exploration, managing corrosion detection in multiple concentric metal pipes using electromagnetic (EM) logging tools is complex due to non-linear signal combinations from multiple nested pipes, requiring advanced methods to accurately interpret metal loss and pipe properties.
Innovation Solution
The use of EM logging tools with inversion methods, combined with adaptive learning algorithms and synthetic modeling, to accurately determine pipe thickness and detect collars in multi-string pipe configurations, enhancing data processing and interpretation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EM logging tools are used to detect corrosion in multiple concentric pipes, then corrosion detection capability is improved, but data interpretation complexity increases due to non-linear signal combinations from multiple nested pipes
Solution Approach 1:
The patent segments the complex multi-pipe signal into individual pipe contributions by performing separate inversions for each pipe. The total measured signal is divided into components from each concentric pipe, allowing independent analysis and interpretation of corrosion in each pipe without interference from other pipes' signals.
Solution Approach 2:
The patent introduces an intermediary inversion algorithm that acts as a mediator between the raw EM signals and the final corrosion assessment. This inversion process transforms the complex non-linear signal combinations into interpretable pipe-specific parameters, including thickness and corrosion indicators for each individual pipe.
2Measurement precision
If advanced inversion methods are used to interpret EM log data from multiple casing strings, then corrosion detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the EM log data and organizing it into pipe-specific signal components before the main inversion analysis. This preparation work structures the data in a way that facilitates faster and more efficient inversion processing, reducing the computational burden during the actual corrosion detection phase.
3Adaptability or versatility
If multiple casing strings are employed in well installations, then operational versatility is improved, but management of corrosion detection operations and data interpretation becomes more complex
Solution Approach 1:
The patent creates a universal inversion framework that can handle any number of concentric pipes with a single integrated approach. This multi-functional system automatically adapts to different well configurations (single pipe, double pipe, triple pipe, or more) without requiring separate methodologies, thereby simplifying operations management while maintaining versatility.
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 allows for precise characterization of pipe integrity and corrosion detection in complex multi-string configurations, improving the accuracy and efficiency of pipe feature inspection and metal loss identification.
Implementation Method 1
broadcasting an electromagnetic field from a transmitter disposed on the electromagnetic logging tool; energizing a casing with the electromagnetic field; recording a secondary electromagnetic field from the casing
Implementation Method 2
an EM logging tool may collect data on pipe thickness to produce an EM log
Data Source
AI summary
A method for identifying an artifacts disposed on concentric pipes may comprise disposing an electromagnetic logging tool into a first wellbore, broadcasting an electromagnetic field from a transmitter disposed on the electromagnetic logging tool, energizing a casing with the electromagnetic field, and recording a secondary electromagnetic field from the casing at a plurality of depths and at a plurality of frequencies. The method may further comprise picking a first plurality of artifacts in the first signal, constructing a target value matrix from the first plurality of artifacts, producing a first input matrix from the first signal and a first well plan, and constructing a predictor from the first input matrix and the target value matrix. Additionally, disposing the electromagnetic logging tool into a second wellbore and producing a second plurality of artifacts from the predictor and the second input matrix.