Development of a Mechanistic and Data-Driven Model for Multiphase Flow Leak Detection in Pipeline
Bibliographic record
Abstract
Abstract Prompt and reliable detection of pipeline leaks is vital for human safety, the economy, the environment, and corporate reputation. However, a simple mechanistic model for accurately predicting leak characteristics in different flow regimes is lacking. To fill this gap, a novel methodology was used to develop a multiphase flow leak detection model using only inlet and outlet parameters. The gas-liquid two-phase leak mass flow rate, location, and size are computed through iterative processes in the upstream and downstream sections of the leak. Data sets were generated for a wide range of geometric (3 – 5 inch pipe diameter, 2000 – 10000 feet pipe length, 500 – 1500 feet leak location, 0.2 – 3 inch leak opening diameter), hydrodynamic (Newtonian, air, CO2, N2) and operating conditions (0.3 – 0.628 outlet liquid fraction). These data sets were utilized to develop contour plots and a data-driven model using statistical analysis based solely on the inlet and outlet parameters. The results indicate that a change in total mass flow rate and pressure in the inlet and the outlet section of the leak can be a good indicator for determining the location and size of the leak. The effect of different pressure constraints, pipe length, pipe diameter, two-phase fluid rheology, leak diameter, leak location, outlet liquid volume fraction, and flowing liquid hold-up on leak size, pressure, and flow rate is analyzed. Decreasing the liquid fraction in the outlet section of the leak leads to a slight increase (6% average) in the inlet mass flow rate and a significant decrease (50% average) in the outlet mass flow rate for fixed pressure constraints, resulting in an increased leak flow rate, pressure, and density. Similarly, longer pipe lengths, bigger pipe diameters, heavier gas phase, and lower liquid fraction at the outlet have higher leak pressure for the same leak locations due to higher leak flow rate. Furthermore, contour plots revealed that identifying a leak near the pipe inlet is easier, although determining its size remains challenging. On the other hand, detecting a leak near the pipe outlet is more difficult, but assessing its size is comparatively easier. The developed data-driven showed good agreement with different literature data sets with a MAPE of less than 20%. The mechanistic model's key advantage lies in its reliance on fundamental equations and physics, making it applicable to various operating conditions for field applications. Moreover, the data-driven model is straightforward and accurate, eliminating the need for complex simulations. This study has the potential to assist industries in determining leak location, size, and pressure using only the inlet and outlet parameters, without requiring multiple sensors along pipelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".