Co-Injection EOR Technology Increases Recovery and Reduces GHG Emissions
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
Abstract Oil demand continues to rise and is not projected to peak until at least 2030, according to the International Energy Agency, or possibly even later as per OPEC. Despite this, many publicly listed oil companies have announced aggressive emissions reduction targets. For instance, Canadian companies Cenovus and CNRL have committed to achieving net zero by 2050. Cenovus aims for a 35% absolute reduction of GHGs by 2035, and CNRL targets a significant reduction from oil sands operations by 2030. CEOs often cite the challenge of generating acceptable returns on emissions reduction projects as a major barrier to decarbonization, as reported by the Boston Consulting Group in 2023. Abatement technologies such as Carbon Capture, Usage, and Storage (CCUS) require substantial investment and are unlikely to yield positive returns for several years. Furthermore, they are expected to remain relatively small in scale and impact in the near to mid-term future, according to the World Economic Forum in 2023. The traditional and conventional method of heavy oil extraction in Alberta and Saskatchewan has been marked by inefficiencies, primarily due to low recovery rates that leave upwards of 90% of the resource untapped. The process is characterized by high emissions and significant capital investment, rendering the development of smaller deposits economically unfeasible. Heavy oil production typically necessitates a substantial upfront capital investment in large, permanent central facilities, leading to a planning horizon of five to ten years for the optimization of a steam plant and production installation. Additionally, securing regulatory approvals for land disturbance and water supply requires considerable time and effort.
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 | 0.000 | 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 it