An Improved Study of Emulsion Flooding for Conformance Control in a Heterogeneous 2D Model with Lean Zones
Bibliographic record
Abstract
Summary Use of oil-in-water (O/W) emulsion has shown its potential for conformance control in heterogeneous porous media, yet it is essential to understand how to improve the conformance control performance in the heterogeneous 2D model with lean zones before it is applied in the fields. In this paper, an O/W emulsion-based conformance control method is improved through newly designed flow tests and optimized modeling study. A heterogeneous 2D model was designed with a high water mobility zone (HWMZ) and a low water mobility zone (LWMZ) separated by a horizontal injection well to mimic real oil sands with lean zones (top- or bottomwater) and with application of horizontal wells. Optimal conformance control strategies were proposed and examined in the 2D model by injecting correspondingly designed O/W emulsions. In an improvement of our previously proposed emulsion flow model (Ding et al. 2020c), we introduce the real phenomena of permeability reduction (PR) coefficients in this paper to describe the three ambiguous coefficients: flow distribution coefficient (γ), plugging coefficient (α), and retention rate coefficient (a). This newly developed model can incorporate with characteristics of the emulsion and the heterogeneous porous media through the introduction of the experimentally derived PR coefficient. It is well established in COMSOL Multiphysics® (COMSOL AB 2005), and the modeling results show good agreement with the experimentally monitored results in the three types of flow tests. This work bridges experimental and mathematical studies on emulsion flow in 2D models associated with lean zones and is able to provide a guide on optimal emulsion design and injection strategy for optimal conformance control performances.
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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 | 0.000 |
| 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".