Automated Downhole Leak Detection in Water Injection Wells
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Solution Overview
Problem
Current methods for detecting leaks in wells are reactive and can take weeks or months, leading to delayed detection and potential damage to wells and the environment.
Innovation Solution
An automated computer-implemented method for downhole leak detection and prediction in water injection wells, using real-time pressure and water rate information from surface and downhole gauges to generate engineered prediction-ready data, detect early signs of leakage, and provide timely notifications to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional reactive surveillance methods are used to detect leaks, then the detection process is simple and does not require complex systems, but the detection time is delayed by weeks or months
Solution Approach 1:
The system performs preliminary actions by continuously monitoring well operation data and calculating predicted values before actual leaks occur. The leak detection system computes expected pressure and rate values based on historical patterns, enabling early detection of deviations that indicate potential leaks, thus reducing detection time from weeks/months to near-real-time
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual well operation data against predicted values generated from historical patterns. When deviations exceed predefined thresholds, the system generates alarms and notifications, creating a closed-loop feedback system that enables rapid response to potential leaks, significantly reducing detection time
2Productivity
If traditional surveillance methods with manual analysis are used, then the system complexity is low, but the productivity and response speed are reduced
Solution Approach 1:
The system performs self-service by automatically monitoring well operation data, calculating predicted values, detecting deviations, and generating notifications without requiring continuous manual intervention. The automated leak detection system processes data, identifies potential leaks, and alerts operators, eliminating manual analysis bottlenecks and significantly improving detection speed and productivity
Solution Approach 2:
The system replaces manual mechanical analysis with automated computational methods. Instead of operators manually reviewing surveillance data, the system uses computer algorithms to calculate predicted pressure and rate values, compare them with actual measurements, and automatically generate alarms, substituting manual processes with automated electronic systems that operate continuously at high speed
3Reliability
If real-time automated monitoring systems are implemented, then the leak detection time is reduced significantly, but the device complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the critical parameters needed for leak detection from the overall well operation data. By focusing on specific pressure and rate measurements and their predicted values, the system avoids processing unnecessary data, reducing computational complexity while maintaining high detection reliability through targeted monitoring of key indicators
Data Source
AI summary
Systems and methods include a method for downhole leak detection and prediction in water injection wells. Bulk well operation information for one or more wells is accessed from a database. Real-time pressure and water rate information is received from surface and downhole gauges during operation of a water injection well. Engineered prediction-ready data is generated using the real-time pressure and water rate information by translating the bulk well operation information. Early signs of leakage at sub-surfaces of the water injection well are determined using the engineered prediction-ready data and wellbore dynamics of the water injection well. Information associated the early signs of leakage is provided based on the determining and for presentation to one or more users.