Automated Creation of the Pipeline Digital Twin During Construction: Improvement to Construction Quality and Pipeline Integrity
Bibliographic record
Abstract
The concept of the digital twin dates all the way back to the 1950’s when NASA, GE and other industrial manufacturers started creating abstract digital models of equipment to model their performance in simulations and maintain a record of the asset throughout its life span [1]. Over the years more and more industries have adopted the digital twin paradigm to improve traceability, maintenance, and analytics allowing for improved sustainment of the asset or equipment while reducing various risks identified during life cycle management. It has been found that collectively, the digital twin concept improves the overall net present value of an asset. The oil and gas industry has slowly been adopting the digital twin paradigm of asset life cycle management over the past two decades with the focus on facilities. Recently, field trials were completed to test and evaluate workflows and sensor platforms for the creation of a digital twin for pipelines. The trials resulted in highly accurate pipeline centerlines, weld locations, Depth to Cover (DoC) and ditch geometry capture in digital formats. This paper describes the methodologies used, and the results of an actual construction field trial with a comparison to traditional data collection methods for these attributes. The value of creating a pipeline digital twin during pipeline construction in near-real-time is discussed with an emphasis on the potential benefits to life cycle management and pipeline integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".