A Novel Approach to Leak Sensitivity Testing of Computational Pipeline Monitoring Systems for Hydrocarbon Liquid Pipelines With Hydraulic Simulators
Bibliographic record
Abstract
In the operation of hydrocarbon liquid pipelines, Computational Pipeline Monitoring (CPM) systems are used for software based leak detection. When installed, CPM systems must meet the regulatory requirements such as API 1130 in the USA and CSA Z662 in Canada. API RP 1130 provides several methods that can be used to test a CPM system: forced parameter testing, simulated leak test (SLT), and fluid withdrawal testing (FWT). Leak tests are performed to establish and verify the leak detection capabilities of the installed CPM system and in some cases test the response of the personnel. One of the primary interests in leak testing is the realism or hydraulic accuracy of the leak signature, in order that the reported leak sensitivity results of the test are reflective of the real performance of the CPM system. Simulated leak tests (SLT’s) use an offline pipeline model to generate hydraulically accurate data which can be fed into the CPM model. SLT’s provide the most flexible and hydraulically accurate solution to simulating leaks, compared to some of the other API RP 1130 compliant test methods. SLT’s do not have leak location restrictions and also correctly models the flow and pressure hydraulic signature of a leak. The paper outlines a novel approach and method to leak simulation, based on its size and shape of the leak hole. This method can be used to represent various sizes of a leak, ranging from a pin hole to a large rupture along the seam. Implementation of the method in a simulator developed with commercial software is discussed. The results of the simulation, namely the hydraulic signatures from the simulated leak and the CPM response, are compared with the widely used leak simulation method using a constant leak rate. Finally, possible applications of this method are considered.
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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".