The application of radio-frequency identification (RFID) technology in the petroleum engineering industry: Mixed review
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it