Cement Integrity Analysis Using Ultrasonic and Sonic Data
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Solution Overview
Problem
Current acoustic measurement methods for diagnosing cement integrity in cased wells, particularly in multiple string casing situations, face limitations due to structural and material properties, as well as interfacial conditions, making it difficult to accurately characterize the annular space between casings and the formation, leading to ambiguity in cement diagnosis.
Innovation Solution
A method combining ultrasonic and sonic data processing with machine learning techniques to determine properties of innermost and outer annuli, using hierarchical Bayesian graphical models and machine learning classifiers to extract features from sonic data, providing a robust characterization of cement integrity by leveraging the independent sensitivities of multiple acoustic modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current acoustic measurement methods are used for diagnosing cement integrity in multiple string casing situations, then the measurement process remains simple and quick, but the diagnosis accuracy deteriorates due to structural and material properties and interfacial conditions causing ambiguity
Solution Approach 1:
The patent segments the cement integrity diagnosis problem into multiple independent analysis components: ultrasonic data processing for innermost annulus properties, sonic data processing for outer annulus properties, and machine learning classification. This segmentation allows each component to be optimized independently while improving overall diagnosis accuracy in complex multiple string casing situations.
Solution Approach 2:
The patent combines multiple acoustic measurement modalities (ultrasonic and sonic) into a composite analysis approach. By integrating data from different acoustic methods with distinct sensitivities to various annular conditions, the system achieves more reliable cement integrity characterization than any single method could provide alone.
2Measurement precision
If multiple acoustic modalities are combined to characterize annular space properties, then the characterization accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary processing layer between the raw acoustic measurements and the final cement integrity diagnosis. This intermediary automatically extracts relevant features from the complex ultrasonic and sonic data, identifies patterns corresponding to different annular fill states, and provides robust characterization without requiring manual interpretation of complex waveforms.
Solution Approach 2:
The patent transforms the raw acoustic measurement data into different parameter representations through machine learning processing. By changing the parameters from raw waveforms to extracted features and classified categories, the system simplifies the interpretation of complex multi-modal acoustic data while maintaining high characterization accuracy.
3Reliability
If machine learning processing is applied to ultrasonic and sonic data, then the cement integrity diagnosis reliability improves, but the computational time and processing resources increase
Solution Approach 1:
The patent performs preliminary processing of ultrasonic and sonic data to extract key features before applying machine learning classification. This preliminary action prepares the data in an optimized format that reduces the computational burden of subsequent machine learning analysis while maintaining the reliability improvements that machine learning provides for cement integrity assessment.
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 enhances the accuracy of cement integrity diagnosis by reducing ambiguity and improving the characterization of annular fill states and bond conditions, providing a more robust and reliable assessment of hydraulic isolation in multiple casing strings.
Implementation Method 1
processing ultrasonic data obtained from ultrasonic measurements on the depth interval of the wellbore to determine properties of the innermost annulus
Implementation Method 2
Sonic data obtained from sonic measurements on the depth interval of the well is processed to extract features of the sonic data
Data Source
AI summary
The present disclosure provides methods and systems for analyzing cement integrity in a depth interval of a wellbore having a multiple string casing with an innermost annulus disposed inside at least one outer annulus. The method includes processing ultrasonic data obtained from ultrasonic measurements on the interval of the wellbore to determine properties of the innermost annulus. The method also includes processing sonic data obtained from sonic measurements on the interval of the wellbore to extract features of the sonic data. The features of the sonic data are input to a machine learning processing to determine properties of both the innermost annulus and the least one outer annulus. Additional processing of ultrasonic and sonic data can also be used to determine properties of both the innermost annulus and the least one outer annulus. These properties can be used to analyze cement integrity in the depth interval of the wellbore.