The application of radio-frequency identification (RFID) technology in the petroleum engineering industry: Mixed review
Bibliographic record
Abstract
Radio Frequency Identification (RFID) technology has emerged as a promising solution for real-time tracking and monitoring in the petroleum industry. This study systematically reviews recent advancements in RFID applications for petroleum asset management, logistics, and safety. The research is based on an extensive review of peer-reviewed literature, industry reports, and experimental case studies involving RFID deployment in refinery operations and pipeline monitoring. The study also examines practical implementation challenges, including signal interference due to metal surfaces, high initial costs associated with infrastructure setup, and integration complexities with existing digital systems such as SCADA and IoT platforms. Furthermore, issues related to data security and the potential for unauthorized access are discussed as critical concerns that need to be addressed for large-scale adoption. Despite these limitations, RFID technology demonstrates significant potential in optimizing supply chain management, enhancing real-time asset tracking, and improving workplace safety in petroleum engineering. The ability to automate inventory management, reduce operational downtime, and enhance predictive maintenance further underscores its strategic importance. Future research should focus on overcoming technical barriers through the development of advanced RFID tags with higher resistance to extreme environmental conditions and improved data encryption techniques. Additionally, cost-effective deployment strategies and interoperability standards must be established to facilitate broader industry adoption. Collaborative efforts between researchers, technology developers, and industry stakeholders will be essential in driving innovation and ensuring the successful integration of RFID into the petroleum sector.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".