Automated Multiphase Flow Model Calibration
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Solution Overview
Problem
Current multiphase flow models in the oil industry struggle to accurately represent simultaneous gas and liquid phase flows, leading to significant systematic errors in pressure and temperature drop simulations, which are not effectively corrected by existing methods, requiring frequent manual adjustments that are time-consuming and inefficient.
Innovation Solution
An automated method using the principle of least squares is implemented to correct systematic errors in multiphase flow models, specifically through a computational algorithm that adjusts pressure and temperature drop factors, reducing the time and improving the accuracy of simulations by stabilizing and converging adjustments across multiple iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of flow models is performed, then the model can be calibrated to match field data, but the process takes too much time and is inefficient
Solution Approach 1:
The patent replaces the manual mechanical adjustment process with an automated computational algorithm. The system uses computer-implemented methods to automatically calculate and apply adjustment factors to flow model outputs, eliminating the need for manual trial-and-error calibration while maintaining or improving accuracy.
Solution Approach 2:
The flow model adjustment system performs self-calibration through automated algorithms that independently calculate adjustment factors based on simulated versus actual field data. The system serves itself by automatically identifying discrepancies and applying corrections without requiring continuous manual intervention.
2Measurement precision
If manual optimization of adjustment factors is performed, then the model can be calibrated, but the quality of results is not as satisfactory as automated methods
Solution Approach 1:
The patent replaces manual optimization operations with automated computational algorithms. The system uses computer-implemented methods to systematically optimize adjustment factors, providing superior result quality while reducing operational complexity through automation.
3Reliability
If flow models are adjusted frequently to account for changing operating conditions, then the model remains accurate, but the periodic adjustment process becomes time-consuming
Solution Approach 1:
The automated adjustment system enables rapid recalibration of flow models in response to changing operating conditions. The system can be re-executed periodically or triggered by condition changes, automatically updating adjustment factors to maintain accuracy without requiring extensive manual effort for each adjustment cycle.
Solution Approach 2:
The system adapts to dynamic changing conditions by allowing adjustment factors to be updated as operating conditions change. The automated nature of the system enables it to respond dynamically to new scenarios, maintaining model reliability throughout the productive life of the system.
Data Source
AI summary
The present invention consists of a method of automatic adjustment of multiphase flow models using the principle of least squares in the correction of the systematic error of simulated pressure drop and temperature drop values. This has been implemented and automated in the form of a computational algorithm and applied in the case study of an actual Production System using Marlim II simulator. For all four multiphase flow correlation sets considered, the adjustments followed each other stably, converging after a few iterations. At the end of the activity, the four sets were found to perform better than the best unadjusted set of correlations. In addition, the method provides consistent results, which is an advantage over the manual adjustment method.Accordingly, the present invention has drastically reduced the time required for optimizing the adjustment factors of flow models and has improved quality of the adjusted model as compared to the final model obtained with manual adjustment. By better quality of the model is meant that the simulated results are closer to the measured results, that is, the model is more capable of representing the flow dynamics verified in the field. In cases with a high number of operating spots in the real system, reduction in the time required by the activity is even more significant.