Rejuvenating Viscous Oil Reservoirs by Polymer Injection: Lessons Learned in the Field
Bibliographic record
Abstract
Abstract Viscous oils have adverse mobility that causes production to decline rapidly following a period of primary recovery. Traditionally, enhanced oil recovery (EOR) for this kind of oil has mostly relied on reducing viscosity and increasing mobility using thermal methods or miscible gas injection. However, in some cases-for example, deep reservoirs, thin sands, or restricted offshore applications-these methods might not be feasible. This paper discusses published field studies using polymer flooding EOR for viscous oil and provides recommendations for its applicability. In countries with reservoirs that present challenges for use of traditional EOR methods, such as Canada, China, and Suriname, polymer flooding has been field tested as a reliable oil-sweeping method while minimizing the risk of high water cut. With oil viscosities at reservoir conditions up to 5000 cp and reasonable economic conditions, polymer flooding has been shown to be an attractive EOR alternative. The study cases presented show that polymer concentrations need only be between 800 to 1,500 ppm to ensure successful results. Concerns about low or poor injectivity of polymers are being overcome using fracturing and horizontal wells, while high-value, limited-space challenges on offshore platforms are being addressed through modular and minimized installations. Additionally, most conventional waterflood monitoring and surveillance techniques are also applicable to polymer floods, while polymer use in backflow tests and as a tracer has also been proposed. This study's projection shows a 90% probability of positive net present value (NPV) under broad ranges of uncertainty for oil price, recovery factor (RF), capital expenditures (CAPEX), and operational expenditures (OPEX). The results of this paper show that polymer flooding presents a clear and feasible alternative for increasing the RF of viscous oil. To this end, a detailed study is provided of its advantages and the reservoir condition range of applicability.</p>
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".