Geological uncertainty and effects of depositional sequence on improved oil recovery processes
Bibliographic record
Abstract
Several emerging improved oil recovery (IOR) techniques have been proposed in the past decades with promising results. However, a systematic study of reservoir heterogeneity on these advanced processes has not yet been presented. This paper provides one of the first comparative evaluations of the effects of reservoir heterogeneity on various IOR processes from the conventional methods (waterflooding, CO2 flooding) to the emerging recovery technologies (Low-Salinity Waterflooding and CO2 Low-Salinity WAG) for wider and more successful implementation of these projects. Since weakness exists in the current simple and unrealistic models, detailed geostatistic models are employed to provide a more realistic and unbiased evaluation of reservoir heterogeneity. A new modeling approach that involves the integration of geological software, a reservoir simulator and a robust optimizer in a closed loop for generating multiple geologically driven realizations and uncertainty assessment of different recovery processes is introduced. Then a series of numerical simulations is conducted to investigate the influences of FU and CU sequences on oil recovery. Finally, the uncertainty range of reservoir heterogeneity is thoroughly evaluated using a large number of geological realizations with significant differences on porosity and permeability distributions. The effect of the K v /K h (aspect) ratio is also addressed in this study. The simulation results indicate that the depositional sequence has a dominant effect on oil recovery in all recovery processes. The CU distribution demonstrates superior performance over the FU distribution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".