Bibliographic record
Abstract
Abstract Given the impact of CO2 emissions on the environment and the direct correlation to the petroleum industry, it has become evident that our industry must minimize the carbon footprint associated with hydrocarbon production and consumption. One of the obvious choices for utilizing CO2 in the industry is associated with enhanced oil recovery (EOR). The injection of miscible/immiscible CO2 EOR is one of the most attractive techniques in these days for oil industry. As part of this investigation, it is important to assess the source/storage of CO2 to ensure reliability and continuous supply. In addition, it is critical to appropriately design the injection rate of CO2, distribution facilities, and the instrumentation for safe operation. In this paper, a case study is completed and facilities design are shown as potential sources of CO2 gas handling process. It is also discussed the potential challenges associated with bulk CO2 storage, compression, transportation and injection. Moreover, an evaluation of existing technologies for CO2 handling facilities is conducted to ensure the desired injection fluid specification. A complete CO2 pipeline network system is developed to determine optimum discharge pressure and design of pipelines specifications is also outlined. The results show that the pump discharge pressure at NGL - CO2 source must be 3,420 psia to meet the 2,850 psia injection wellhead pressure. Also, the ANSI-2500 piping class meets the high injection pressure requirement. In addition, the pipeline network simulation model shows that the optimum pipeline size should be 8 in. The developed design of Case study CO2 injection facilities is the first CO2 injection project in the company's history. This case study along with its finding will greatly help in controlling the CO2 emission in the environment.
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".