Testing geological heterogeneity representations for enhanced oil recovery techniques
Bibliographic record
Abstract
Abstract This paper analyzes the effects of geological heterogeneity representation in a producing reservoir, when different stochastic simulation methods are used, so as to assess the consequent effects on flow responses for different enhanced oil recovery (EOR) techniques employed. First, the spatial heterogeneity of a fluvial reservoir is simulated using three different stochastic methods: (1) the well-known two-point sequential Gaussian simulation (SGS), (2) a multiple-point filter-based algorithm (FILTERSIM), and (3) a new alternative high-order simulation method that uses high-order spatial statistics (HOSIM). Numerical results show that SGS suffers from the inability of describing the highly permeable channel network whereas FILTERSIM better reproduces this connectivity. By means of the recent HOSIM, a more appropriate description of the curvilinear high-permeability channels is obtained. Second, the realizations generated above represent permeability fields in EOR numerical simulations. In particular, four different methods are considered, namely: (1) surfactant, (2) polymer, (3) alkaline-surfactant-polymer and (4) foam flooding processes. The numerical results show that properly reproducing the main geological features of the reference images has a higher impact if surfactant or alkaline chemicals are injected rather than polymer or foam. This is due to the fact that these latter chemicals act by mitigating the effects of heterogeneities.
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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 | 0.001 |
| 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".