The Internet of Things in the Oil and Gas Industry: A Systematic 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
The low oil price environment is driving the oil and gas (O&G) industry to become more innovative and deploy smart field technologies, to increase operational and asset efficiency, minimize health, safety, and environmental (HSE) risks, improve asset portfolio, reduce capital and operation costs, and maximize capital productivity. The Internet of Things (IoT) is at the forefront of this digital transformation, enabling seamless real-time data collection, processing, and analysis from a range of equipment, processes, and operations to achieve these objectives. There are various operations/applications in the upstream, midstream, and downstream sectors (e.g., condition-based monitoring and location tracking) for which IoT-enabled solutions have a significant impact and offer a range of opportunities to increase socioeconomic benefits. However, there are several impediments (e.g., vulnerability to cyber attacks, lower technological readiness for deploying in zone-0 and zone-1 hazardous environments, unavailability of communication infrastructure, labor concerns, and maintenance and obsolescence) that slow the pace of adoption of IoT technologies for regular upstream, midstream, and downstream operations. This review article provides an overview and assessment of the role, impact, opportunities, challenges, and current status of IoT deployment in the O&G industry.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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