Water Control Diagnostic Techniques

1. Log-Log Plots

- Plotting WOR and GOR on log-log scales reveals distinct trends, making it easier to diagnose production problems.

2. WOR Derivatives

- Analyzing the derivatives of WOR helps differentiate between water coning and multilayer channeling.

3. Numerical Simulations

- Systematic simulations support the development of these diagnostic plots, enhancing their accuracy and reliability.

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We will discuss the second method "WOR derivatives using chan plots"

The figure below is the summary of chan plots using production data

Recent Advanced Models for Diagnosing Excessive Water Production

1. Chan Plot: The Chan plot is emphasized as a key diagnostic tool but is limited by manual interpretation challenges and noise in data.
2. Machine Learning: Machine learning models have been developed to improve Chan plot interpretation, though these models have limitations, especially in detecting multiple patterns over time.
3. Integration with Other Tools: Combining Chan plots with other diagnostic tools can improve accuracy in identifying water production patterns.
4. Slope Analysis: Slope-based features have been identified as crucial in improving the accuracy of machine learning models for diagnosing water production issues.
5. Challenges in Current Models: The paper identifies challenges in current models, including their inability to detect multiple patterns and their sensitivity to data noise.
6. Real-Time Monitoring: There is a need for real-time monitoring models that can continuously track changes in water production patterns to prevent excessive water production.
7. Outlier Detection: Improved outlier detection methods are needed to enhance the accuracy of water production diagnostics.
8. Future Research Directions: The paper suggests future research should focus on developing interactive models for early detection of water-related issues.
9. Potential for AI: The potential for AI to standardize and automate Chan plot interpretation is highlighted, though further advancements are needed.

Table 1: Summary of studies on Chan plot Integration with other diagnostic tools

Table 2: Summary of Chan’s Improvement Attempts