Bibliographic record
Abstract
Chemical-based enhanced oil recovery (EOR) techniques utilize the injection of chemicals, such as solutions of polymers, alkali, and surfactants, into oil reservoirs for incremental recovery. The injection of a polymer increases the viscosity of the injected fluid and alters the water-to-oil mobility ratio which in turn improves the volumetric sweep efficiency. This research study aims to investigate strategies that would help intensify oil recovery with the polymer solution injection. For that purpose, we utilize a lab-scale, cylindrical heavy oil reservoir model. Furthermore, a dynamic mathematical black oil model is developed based on cylindrical physical model of homogeneous porous medium. The experiments are carried out by injecting classic and novel partially hydrolyzed polyacrylamide solutions (concentration: 0.1-0.5 wt %) with 1 wt % brine into the reservoir at pressures in the range, 1.03-3.44 MPa for enhanced oil recovery. The concentration of the polymer solution remains constant throughout the core flooding experiment and is varied for other subsequent experimental setup. Periodic pressure variations between 2.41 and 3.44 MPa during injection are found to increase the heavy oil recovery by 80% original-oil-in-place (OOIP). This improvement is approximately 100% more than that with constant pressure injection at the maximum pressure of 3.44 MPa. The experimental oil recoveries are in fair agreement with the model calculated oil production with a RMS% error in the range of 5-10% at a maximum constant pressure of 3.44 MPa.
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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".