Analysis and Correlations of Viscous Fingering in Low-Tension Polymer Flooding in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Post-primary recovery from some mobile heavy-oil reservoirs in Western Canada cannot be improved using thermal methods due to environmental concerns and technical difficulties. Moreover, miscible gas injection suffers from low initial production rates, premature breakthrough, and possible, formation damage. Low-tension polymer flooding (LTPF) can be an alternative in these reservoirs. However, a major technical challenge in LTPF is that a fingered displacement front may occur. This instability reduces displacement efficiency and may invalidate normal method of simulating LTPF performance based on relative permeability and capillary pressure concepts. Also, it introduces an additional scaling requirement for using results of experimental tests in larger scales. Therefore, it is important to predict the nature of instability, to avoid viscous fingering, or, where it is inevitable, to be capable to include it as an additional factor in modeling displacement. Previous experiments of viscous fingering in immiscible displacements have been conducted in presence of high single-phase permeabilities and linear displacement schemes. The question is whether previous findings are valid in displacement schemes similar to oil-field patterns (e.g., five-spot) in which one should deal with varying velocity profiles from injector(s) to producer(s). Hence, the effect of dispersion caused by varying velocity profiles has not been tested completely on viscous fingering. To help understand viscous fingering in LTPF in heavy oil reservoirs and to overcome the aforementioned limitations, we conducted experiments in low-permeability, one-quarter five-spot patterns. Foremost parameters including oil recoveries at different times to breakthrough, ultimate oil recovery, pressure drops, cumulative saturation profiles, mean local saturations, fingers length and width, dynamic level of bypassing, dynamic population of fingers, rate of growth of population of fingers and number frequency of the fingers were measured. We have correlated some of these parameters with displacement time and front position. Analysis of experimentally-observed fingering patterns of LTPF in this study is the most detailed interpretation performed to date, which provides new insights into the onset of fingering and fingers development. Results would help numerical simulation and stability theory to satisfactorily reproduce qualitative and quantitative features of finger growth.
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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 | 0.001 | 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".