Polymer Flood Technology For Heavy Oil Recovery
Bibliographic record
Abstract
Abstract The concept of polymer flooding heavy oil reservoirs for improved oil recovery was investigated by Knight & Rhudy1 as early as 1977. The practical application of this concept was not really feasible until two events occurred:the widespread use of horizontal wells to provide economic heavy oil production rates andrelatively high oil prices. This paper reviews the polymer flood fundamentals as they are applied to heavy oil recovery. The theory is supported with coreflood and physical model results for several oils ranging in viscosity from 300 mPa.s to 1600 mPa.s. For some of these coreflood tests the polymer flood was able to double the oil recovery in comparison to a baseline waterflood. In order to demonstrate the impact of the polymer flood technology on a field scale, simulations were conducted on a model reservoir. The simulated polymer flood results, as secondary or tertiary recovery mechanism, are compared to a baseline waterflood performance. Applying the polymer technology in combination with "Face to face", parallel, horizontal wells allows for high injection rates of the viscous polymer solution and economic production rates of the heavy oil. A simple economic analysis highlights the economic potential of the polymer flood technology in comparison to a waterflood. Under suitable conditions the polymer flood technology can nearly double the waterflood recovery. Introduction The vast heavy oil formations of Western Canada and in particular of Alberta offer tremendous challenges for reservoir and production engineers. In particular, thin heavy oil formations with oil viscosity between 100 and 2000 cp present unique challenges and opportunities for additional recovery beyond the primary production2. Such formations are considered too thin for applications of gravity drainage processes like SAGD and Vapex. The relatively low viscosity (compared to heavier oils and bitumen) and the high permeability of these unconsolidated sand formations combine to make the oil mobile enough at the reservoir conditions to make displacement processes, like waterflooding, technically feasible. Waterflooding has been tried in such reservoirs with limited success. The biggest problem encountered in waterflooding is the poor sweep efficiency and very rapid increase in the water/oil ratio in the produced fluid3. Indeed, most of the oil in such waterflooding projects has been produced at very high water/oil ratios, requiring very large scale recycling of the produced water. The economics of such production are, at best, marginal. Naturally, it is desirable to examine ways of reducing the produced water/oil ratio. When waterflooding viscous oil reservoirs, the unfavorable mobility ratio between the injected water and displaced oil generates a very unstable displacement front resulting in viscous fingering/channeling and poor sweep efficiency. Preferential flow paths establish themselves very quickly between the injector and producer, conducting most of the water without recovering significant amounts of oil. The waterflooding potential can be improved significantly by increasing the viscosity of the injected water, thus generating a more favorable mobility ratio. It is generally accepted that shifting the mobility ratio in a favorable direction improves the sweep efficiency on a reservoir scale, thus enhancing the oil recovery4.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".