Bibliographic record
Abstract
Abstract Enhanced Oil Recovery (EOR) has been utilized in Trinidad and Tobago for over 50 years. Most projects so far have focused on thermal as well as gas injection along with the more conventional waterfloods. In spite of that, recovery factors are still relatively low and the country's oil production has been declining for some time. Surprisingly, given the progress in chemical EOR and in particular polymer flooding in the last 10 years, these processes have not been used in Trinidad and we suggest that it might be time to consider their application. Similarly, foam has been used extensively worldwide to improve performances of gas and steam injection but has not yet been used in the country. The situation of EOR in Trinidad will be first reviewed along with the characteristics of the main reservoirs. Then the potential for the application of chemical-based EOR methods such as polymer, surfactant and foams will be studied by comparing the characteristics of Trinidad's reservoirs to others worldwide which have seen the applications of chemical-based EOR methods. This review and screening suggests that there is no technical barrier to the application of all these EOR methods in Trinidad. Most reservoirs produce heavy oil and are heavily faulted, but polymer injection has been widely applied in heavy oil reservoirs as well as in faulted reservoirs before, and suitable examples will be provided in the paper. Similarly, these characteristics do not present any specific difficulty for foam-enhanced gas or steam injection. The main issue appears to be the identification of suitable water sources for the projects. This paper proposes a new look at EOR opportunities in Trinidad using conventional methods which have not been used in the country. This will help reservoir engineers who are considering such applications in the country and hopefully will eventually result in an increase in the oil production in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".