Selecting reliable and cost effective power systems for Oil & Gas facilities
Bibliographic record
Abstract
The reliability of power systems is critical for Oil & Gas facilities. Traditionally, the process technology used in such facilities is very sensitive to power interruptions. This paper presents a methodology for selecting reliable and cost effective power systems solutions for Oil & Gas facilities using conventional as well as advanced electrical power technologies. Historically, the design of industrial power systems is based on prior engineering experience and knowledge. With the development of new power technology products and systems, such approach is not always the best and most optimal solution for the industrial power systems. A practical methodology for selecting reliable and cost effective power systems for Oil & Gas facilities is proposed in this paper. The implementation of the power system depends of the specific end user requirements, the technology process in the facility, and the limits imposed by the power distribution utility. The methodology allows for comparison of different power system configurations as well as the same type of configurations implemented with different technologies available in the market — AIS, GIS, hybrid, etc. These configurations are estimated using multiple criteria such as reliability, cost of poor reliability, operation and maintenance costs, initial investments, land acquisition and preparation costs. For any of the proposed configurations all of these criteria are combined into the Life Cycle Cost, (LCC). Ultimately, the proposed power system alternatives can be ranked by the end user in order to select an optimal power system solution for the Oil & Gas facility. The proposed methodology is applied for selecting an optimal power system for a typical Oil & Gas facility in Alberta, Canada. The results show the impact of a combined criterion including the Life Cycle Cost together with the end user's subjective preferences on selecting a reliable and economically efficient industrial power system. The paper illustrates the importance of power system configurations and the applied technology on reliability and the Life Cycle Cost of the project.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".