Comparison of Steam and Polymer Injection for the Recovery of Heavy Oil
Bibliographic record
Abstract
Abstract Steam injection (including cyclic steam and SAGD) has long been recognized as the favored recovery method for heavy oil, with applications in many fields around the world in particular in California and Canada. More recently, polymer flooding has also become a relatively well accepted method to increase production and recovery in heavy oil fields. Numerous successful pilots have been reported these last few years and field expansions are currently ongoing in Canada, Oman, China and Albania for instance but surprisingly enough, there has been to the best of the author's knowledge no such application in the US. Both steam and polymer injection have their advantages and their limitations and simple screening criteria have been developed by several authors, however there has never been a detailed comparison of the two methods and this is what this paper proposes to do. The pros and cons of both steam injection and polymer flood are reviewed in light of fundamentals and field experience: reservoir depth, thickness, oil viscosity, expected recovery, water usage and economics of both processes (in particular capital requirements) are all addressed. Guidelines are then provided for the selection of the right process given the reservoir conditions and the capital constraints. Results show that while steam injection can achieve much higher recovery than polymer flood and is also applicable in much higher oil viscosity, polymer flooding is not limited by depth or reservoir thickness, has lower operating costs and is also less capital intensive. Thus, there is a large opportunity to develop heavy oil reservoirs using polymer where steam injection is not possible. Delamaide and Euzen (Delamaide & Euzen, 2014) estimated that in the US alone, over 5 billion bbl of oil could be targeted by this technique. This paper will provide guidance to engineers who need to select the optimum Enhanced Oil Recovery method to apply in given heavy oil fields, going beyond the standard screening criteria. It will also increase awareness on the possibilities of polymer flooding in some reservoirs, with a significant potential target not only in the US but also worldwide.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".