Feasibility Study Of Compressed Natural Gas (cng) Application In Oil And Gas Operations
Bibliographic record
Abstract
This study conducted for Imaginea Energy takes the form of a high level assessment of the energy, environmental and economic impacts of making a clean fuel switch away from diesel/gasoline and propane to locally sourced Compressed Natural Gas (CNG). Given the affordability and abundance of natural gas in Canada, this study assesses whether a clean fuel switch is worth making. This study is conducted through the help of CNG industry professionals, guidance from Imaginea Energy and references from several literature reviews that shed light on the application of CNG. There are three proposed alternatives in this study from which a recommendation will be made. The three alternatives include Alternative 1: Status Quo, Alternative 2: Switch to CNG with Vendor and Alternative 3: Switch to CNG without Vendor. While Alternative 2 and 3 have the lowest GHG emissions, Alternative 3 was the recommended alternative given its reduction in energy intensity as well as a shorter payback period. It should be noted that this recommendation was made within the context of this study, which had several limitations as well as assumptions. As such, a more micro-level assessment should be conducted in order to strengthen this study’s recommendation.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".