Predicting offshore oil and gas pipelines condition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crude oil and gas products transported using pipelines systems is safe and economical all over the the world. Nonetheless, such pipelines can still be subject to various degrees of failure and degradation generating hazardous consequences and severe environmental damages. As a result, it is important for these pipelines to be effectively monitored and assessed for optimal operation. Many models have been developed to predict pipeline failures and conditions. However, most of these models were limited to use corrosion features as the only factor to assess the condition of pipelines which can lead to inaccurate condition prediction. Therefore, the main aim of this paper is to develop models that predict the condition of offshore oil and gas pipelines based on several other factors including corrosion. Regression analysis and artificial neural network (ANN) techniques were used to develop condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on experts' opinion. All necessary statistical diagnosis have been checked showing sound results for the developed models. The models have been validated and the results showed their robustness with an average validity percentage from 96 to 99%. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspections and rehabilitation planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 it