Selection of Proper Criteria in Flow Behavior Characterization of Low Tension Polymer Flooding in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Applying a suitable enhanced oil recovery method to heavy oil reservoirs is still controversial. Low Tension Polymer Flooding (LTPF) can be considered when thermal and solvent-based methods are not feasible due to technical difficulties and/or economical/environmental constrains. Performance of LTPF, which uses cost-effective surfactant with dilute concentrations within the injected polymer solution, is noticeably far superior to that of alkali-surfactant flooding which suffers from sever viscous instability and/or weak in-situ surfactant generation due to inappropriate acid number of some heavy oils. To properly characterize the microscopic and macroscopic flow behavior of LTPF in heavy oil displacement, the interplay between viscous, capillary, and gravitational forces should be identified by utilizing bond and capillary numbers. Unlike bond number, the capillary number has been defined in several forms by researchers many of which are not equivalent. An appropriate definition of the capillary number should be correctly selected and employed–according to the interest in scale and fluid-flow behavior–so as to quantify the impacts of selected capillary number on the hydrodynamic instability, relative permeability shifts, and phase trapping and bypassing in LTPF in heavy oil displacement. In this study, the capillary number definitions (i.e., pore-scale, Newtonian-fluid, and apparent capillary numbers) were evaluated to determine an appropriate capillary number definition to quantify the LTPF in heavy oil displacement by employing physical modeling and numerical simulation. Moreover, the effects of pore size distribution and injection flowrate on above-mentioned three different capillary number definitions and their sensitivity to the change in those parameters were examined. It was inferred that the sensitivity of change in the pore-scale capillary number is appropriate to evaluate the phase trapping and bypassing whereas the apparent capillary number should be used to characterize the macroscopic behavior (e.g., relative permeability shifts, hydrodynamic instability, and recovery versus capillary number curves) of LTPF. The effects of the pore size distribution and injection flowrate on phase trapping and bypassing, which were rendered, can be considered as a practical guide to build a robust trapping model which is the key to properly simulate and optimize the entire LTPF in lab and field scales.
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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 | 0.002 |
| 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 | 0.001 |
| 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".