EOR Methods Applicability Analysis for Heavy Oil Reservoirs under Polar Circle Conditions
Bibliographic record
Abstract
Abstract The current work is focused on definition of development strategies applicability for Russkoe heavy oilfield located above Polar Circle. For this authors collected the world-wide experience of heavy oilfield development approaches supplied with permafrost zones and systemed the results obtained on Russkoe oilfield. Series of investigations result in the fact that there is no an exact analog for Russkoe oilfield. However analog oilfields can be assumed for separated zones of Russkoe oilfield. Russkoe oilfield is characterized by a unique combination of factors having a critical influence on the choice of EOR methods. High oil viscosity, multiple faults within a formation, extensive water-oil and gas-oil saturated zones, great thickness and vertical heterogeneity of target layers, permafrost (to 500 m depth), low reservoir temperature, poor cemented sandstone rocks. Authors investigated world heavy oilfields, systemed its experience, compared geology for analogs, formed statistics of the effectiveness of EOR methods applicability. Several regions of heavy oilfields were defined. First of all it is heavy oilfields with permafrost located in Canada, China, USA and Russia, in the second turn it is non-permafrost zone heavy oilfields located in Venezuela, central part of the USA and Kazakhstan. As a result world experience of heavy oilfield development is summarized, geological particularities are compared, the applicability of methods projected on Russkoe oilfield.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".