Wellbore Leak Detection Using Acoustic Logging and Edge Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately identify and isolate specific leaks in wellbore zones due to noisy interference from varying sound frequencies and intensities, and the complexity of translating acoustic waves into fluid flow information, making it difficult to pinpoint undesired fluid flow sources.
Innovation Solution
A method and system utilizing a bottom hole assembly with an acoustic logging tool, coiled tubing, and inflatable packers, combined with edge computing and artificial intelligence, to detect and isolate leaks by analyzing acoustic signal data in real-time, correlating it with temperature profiles, and deploying packers to seal the leak interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If acoustic signal data is collected from the entire wellbore zone, then the coverage area is improved, but the measurement precision deteriorates due to inability to identify specific leak sections
Solution Approach 1:
The wellbore zone is divided into multiple discrete measurement sections along the wellbore path. The acoustic logging tool captures data from specific depth intervals rather than treating the entire zone as a single unit, enabling identification of leak locations within particular segments. This segmentation allows precise localization of leaks while maintaining comprehensive coverage of the entire wellbore.
2Difficulty of detecting and measuring
If acoustic waves are used to detect fluid flow, then the detection capability is improved, but the difficulty of detecting and measuring increases due to noisy interference from varying sound frequencies and intensities
Solution Approach 1:
The system applies different analysis methods to different frequency components and signal characteristics. By analyzing acoustic signals with varying properties (frequency, intensity, waveform) using tailored processing techniques, the system can distinguish leak-related acoustic signatures from background noise. Temperature profile correlation further enhances this by providing additional context for validating acoustic detections.
Solution Approach 2:
The system uses real-time analysis of acoustic signal characteristics to adjust detection parameters and filter settings. By continuously monitoring signal quality and noise levels, the system can adaptively enhance detection sensitivity in low-noise conditions and suppress false positives in high-noise environments, effectively managing the harmful interference.
3Speed
If real-time analysis is performed using edge computing, then the speed of detection is improved, but the device complexity increases due to integration of multiple components and computing requirements
Solution Approach 1:
The system combines the acoustic logging tool, temperature sensors, coiled tubing, inflatable packers, and edge computing capabilities into an integrated bottom hole assembly. This merging of multiple functions into a single deployable unit enables real-time leak detection and response without requiring separate equipment or multiple trips, thereby managing complexity through functional integration rather than proliferation of separate systems.
Solution Approach 2:
The bottom hole assembly performs self-diagnosis and self-response operations. The real-time acoustic and temperature data analysis automatically identifies leaks, and the system can autonomously deploy inflatable packers to isolate detected leaks without requiring continuous surface intervention. This self-service capability reduces the operational complexity of managing the sophisticated detection system.
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
Enables precise identification and isolation of leaks within wellbores, improving the accuracy of fluid flow analysis and predicting the impact on oil production, thereby enhancing wellbore efficiency and reducing unwanted fluid loss.
Implementation Method 1
Acoustic signal data from within the wellbore is detected using the acoustic logging tool
Implementation Method 2
a fluid is pumped through the coiled tubing of the bottom hole assembly to inflate the one or more packers to isolate the leak interval
Data Source
AI summary
Described is a system for identifying leaks in a wellbore. The system includes a bottom hole assembly having an acoustic logging tool, coiled tubing, and one or more inflatable packers. The system further includes a computing device with an artificial intelligence analyzer having edge computing capabilities. When the bottom hole assembly is run into the wellbore, the acoustic logging tool is configured to identify a leak interval using acoustic waves. Data associated with the leak interval is transmitted to the computing device to be analyzed using the edge computing capabilities. A leak is identified within the leak interval upon analysis of the data. In response to identifying the leak, the one or more inflatable packers are deployed to seal the leak in the leak interval.