Bibliographic record
Abstract
High oil prices and concerns about future oil supply are leading to a renewed interest in enhanced oil recovery (EOR), a group of technologies that can significantly increase recovery from existing oil reservoirs. Most of the experience with EOR is still in the United States, principally with CO2 flooding in the Permian Basin in west Texas and with the several thermal processes in the San Joaquin Valley in California. A listing of these projects is compiled every 2 years. But worldwide applications are growing. Thermal recovery of bitumen in Alberta, Canada, is increasing rapidly, and thermal projects have been successful in Venezuela, Indonesia, and elsewhere. Chemical and polymer floods are being implemented in China. New applications increasingly will be worldwide. Each one will depend on careful planning to design an EOR project specific to the properties of the oil, the reservoir conditions, and the availability of injectants. In many situations, new EOR technology will be necessary. The processes being applied in the United States were tailored for those conditions and do not necessarily translate to other geologic provinces. This article attempts to distill past experience to define the state of the art in planning EOR projects. It is grounded in more than 30 years of experience by the authors in a wide variety of EOR applications. The Planning Process Successful EOR project management depends on good planning. “Prior proper planning prevents poor performance,” they say, and it is especially true when EOR is involved. Planning includes: - Identifying the appropriate EOR process. - Characterizing the reservoir. - Determining the engineering design parameters. - Conducting pilots or field tests as needed. - Finishing with a plan to manage the project to meet or exceed expectations. From the outset, and at every step along the way, we strongly recommend that careful attention be paid both to economic studies and to reservoir simulation as the reservoir characterization and engineering design progresses. In this way, the chances of success are greatly improved. Fig. 1 illustrates the interaction of all three. Economics is the ultimate project driver. After all, unless the project is comfortably profitable, it should not be pursued in the first place. But reliable economics need good performance predictions. Good simulation models need good data. And what data are needed is determined by which project elements the economics is sensitive to. Each guides and depends on the others.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".