Application of Error-in-Variable Model (EVM) for Estimating Gas Pipeline Internal Wall Roughness Before and After Pigging
Bibliographic record
Abstract
Gas pipeline internal surface typically undergoes degradation for a variety of reasons such as fouling of the pipe inner surface, erosion, corrosion and deposits of objectionable materials that occasionally enter the gas stream at receipt points. Accurate monitoring of the pipe internal surface condition can hugely benefit the planning of cleaning activities. Theoretically the pipe wall roughness for a given pipe segment can be extracted based on measured flow data and other system parameters. The challenge lies in the fact that measured data all contain varying degrees of uncertainty, and the system becomes more complex to analyze when it contains different segments connected in series or parallel like many typical gas gathering and lateral networks. This paper demonstrates the application of the Error-in-Variable Model (EVM) using the Markov Chain Monte Carlo (MCMC) solution method in analyzing a complex pipeline network on the TransCanada NGTL System. EVM, a well-established Bayesian parameter estimation technique, accounts for uncertainties in the measured variables, such as flow and pressure data, when determining the most probable estimates of unknown parameters such as pipe internal wall surface roughness. In this work, the EVM problem is solved using the MCMC Metropolis-Hastings algorithm. The MCMC approach is demonstrated to be robust, easy to implement and capable of handling large quantities of data. It has the potential to analyze complex networks and monitor the pipe wall surface condition on-line with SCADA data. Using this method, the internal wall surface roughness for the segments of interest in this network were extracted from measured data collected before and after the pigging operation. Results demonstrate the model’s capability in estimating the degradation of the pipe wall internal surface and the effectiveness of pigging. Details on implementation and challenges in applying such methodology to analyze complex gas networks are discussed.
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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".