Challenges in Inter-organizational Knowledge Transfer for the Life Extension of Oil and Gas Facilities
Bibliographic record
Abstract
The ageing process of oil and gas facilities poses unique challenges in risk management, especially when operators have the intention to extend their service life. Facility extension has been an object of increased interest in the oil and gas industry because of its benefits. Researchers have identified several organizational issues that can impact this process. Among these, knowledge transfer is a critical aspect in contexts involving facility transfer between companies. The goal of this research is to investigate the inter-organizational knowledge transfer (IKT) elements and mechanisms of oil and gas facilities acquired for life extension and understand their main challenges. A qualitative case study was carried out on the transfer of an oil and gas offshore production facility between companies. The study identified 22 key elements and 27 challenges that the acquiring operating company faced during the IKT process. This case study provides valuable insights that can guide other organizations in similar situations, helping them better manage the IKT process, mitigate potential risks, and ensure smoother operations during and after facility transfer. It can also support the development of future frameworks by managers and oil and gas regulators to evaluate IKT issues as part of oil and gas facility life extension.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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".