Efficiency of Horizontal and Vertical Well Patterns on the Performance of Micellar-Polymer Flooding in Anisotropic Reservoirs
Bibliographic record
Abstract
There is increasing interest in micellar-polymer flooding because of the need to increase oil production from depleted and waterflooded reservoirs. Using horizontal wells for injection and production in a micellar-polymer flood process, higher sweep efficiency is expected compared with the use of conventional patterns by vertical wells. However, the use of horizontal wells is very sensitive to the well pattern designed to operate the process. This paper presents an analysis of how the overall performance of a micellar-polymer flood process in anisotropic reservoirs is influenced by the well pattern using horizontal injector and producer in different configurations. A three-dimensional numerical simulator for fluid flow and mass transport is used to analyze the effectiveness of well combinations in micellar-polymer applications. The potential for a horizontal well application was assessed through different situations in combinations of injection and production wells and degree of reservoir anisotropy. Results from the study have demonstrated that significant amount of oil can be recovered additionally and injectivity was remarkably improved by utilizing a combination of horizontal wells. The improvement of injectivity through a horizontal injection well was higher when it was combined with horizontal producer parallel to the injector. The overall performances in anisotropic reservoirs strongly depend on the type of wells considered and the orientation of the horizontal wells with respect to the permeability directions. Combination of horizontal wells placed parallel to the low permeability direction yields the best performance. In high permeability ratio reservoirs, the presence of horizontal injectors is more significant in defining the efficiency of the micellar-polymer flood than the horizontal producers. Key words: Micellar-polymer flood; Horizontal well; Anisotropy; Injectivity
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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 | 0.001 |
| 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) | 0.001 | 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".