Selecting Injected Viscosity in Polymer Flood Projects: A Controversial and Critical Question
Bibliographic record
Abstract
Abstract Polymer injection is now a mature EOR process, and numerous large-scale expansions are currently underway while new projects are being designed all over the world. Curiously, one of the basic design questions still remains highly controversial: what is the optimum viscosity that should be injected? Some practitioners advocate injecting very high viscosities while others advocate just the opposite. The selection of the viscosity to inject has obvious economic implications as it is directly linked to the polymer concentration and thus to the cost of the polymer which can reach tens or hundreds of millions of dollars for large expansions. This paper will explain why the question still remains without a clear answer and will describe the arguments of both camps while outlining the pros and cons of each approach using case studies. The paper reviews the theoretical and practical arguments based on real field experience to help future project designers select the right viscosity for their polymer project. This is a critical issue as this can have an impact on injectivity and economics. The Gogarty method is a theoretical tool to select polymer viscosity, but it is extremely conservative and may lead to over-design. Reservoir simulations have also been used in several cases to justify extremely high polymer viscosities but in some cases field results do not bear out these expectations. The conclusions of this work show that several factors need to be considered when selecting polymer viscosity; beyond injectivity and mobility control which are obvious ones, another important factor is the reservoir layering. Field experience shows that in single layer reservoirs such as those in Canada, lower viscosities can be used but that in cases of heterogeneous, multi-layer reservoirs, higher viscosities are required. However, theory demonstrates that even when injecting infinite polymer viscosity, vertical sweep will remain controlled by the permeability contrasts. Finally practical concerns for expansions should not be forgotten: practical experience in Daqing for instance shows that injecting at high viscosity can cause severe casing and vibration issues, while theory and practical experience in other fields both confirm that produced polymer concentration could cause severe issues in the surface facilities. Reservoir and surface aspects need to be considered with the view that even when designing a pilot, large-scale expansion is the ultimate goal that needs to be kept in sight. Expansions require not only successful pilots but also attractive economics and will present challenges beyond those experienced in a pilot such as separation issues in the surface facilities. The paper will provide some guidance for the design of their future projects and provide the context for making such decisions in the framework of large-scale field projects.
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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 | 0.036 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".