Predictive Corrosion Degradation Modelling of Oil Country Tubular Goods Using Physics-Informed Machine Learning and Digital Twin Technologies
Bibliographic record
Abstract
This study provides a comprehensive research methodology to accurately determine the degradation and remaining life of Oil Country Tubular Goods (OCTG) under sour and high-temperature conditions.Experiments were conducted to determine the properties of three materials (API X70 Carbon Steel, 22Cr duplex stainless steel, and Inconel 625).The results confirmed that Inconel 625 possessed the maximum hardness and resistance to corrosion, followed by 22Cr duplex stainless steel and API X70 Carbon Steel.A physicsinformed long short-term memory (PI-LSTM) algorithm was developed for physically grounded estimation by incorporating Paris's law and Arrhenius models for corrosion.The algorithm outperformed all conventional models (accuracy-93% and R -0.92), enabled reliable life estimation in digital twin simulation platforms, and allowed precise estimation of degradation performance 10% accuracy, replicating laboratory experiments.The digital twin simulation estimated the pipeline health index values to provide reliable predictive maintenance services.The methodology provides efficient research for scaling intelligent integrity management systems for OCTG under adverse operational conditions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".