Uncertainty Quantification in Well Test Metering

Summary

This project focused on quantifying uncertainty in well test measurements, with a particular emphasis on watercut meter error at well testing facilities. The analysis demonstrated the significant impact of metering error on field-level production estimates and explored Bayesian methods to integrate multiple measurements across the field production network, improving the estimation of actual production rates.

Project Highlights

• Analyzed error in watercut meters and its effect on well test and field analysis
• Explored Bayesian approaches to combine measurements from different locations in the production network for more accurate rate estimation
• Provided recommendations to inform metering calibration guidelines and highlighted the value of maintaining high-quality field metering
• Laid the foundation for future metering related efforts

Technical Innovation

• Applied Bayesian inference to quantify uncertainty and integrate hierarchical measurement data
• Demonstrated the impact of metering error on field analysis and decision making
• Developed analytical workflows to support calibration and metering prioritization

Impact

Although the project was sidelined due to shifting priorities, it provided valuable insights into the importance of metering accuracy and calibration. The work established a foundation for future work and operational decisions.