Bibliographic record
Abstract
Technology Focus The bulk of the literature on enhanced oil recovery (EOR) from the past year has been devoted to an improved understanding of trends started more than a decade ago with physical and numerical modeling. A quick review of the basics will be helpful for putting these developments in perspective. The concept of displacement in an EOR method requires bringing the mobility ratio of displacing fluid to that of displaced oil under unity [Kw µ o / Ko µw less than 1; Ko, Kw being relative permeabilities of displaced oil and displacing fluid (e.g., water), respectively, and µ o, µw being their respective viscosities], and increasing sweep efficiency (Es) of the displacement process. Various established EOR methods try to accomplish this in different ways, depending on the formation and oil types, aiming to keep costs and environmental impact down while obtaining high and quick recovery. Thus, polymers or foams have been used traditionally to increase viscosity of displacing fluids (µw). To reduce Kw/Ko, surfactant; alkaline; microbes, which create in-situ surfactants; or low-salinity water, which changes wettability to water, were used. Steam or hot water have also been used to reduce both Kw and µo. Miscible gas or solvent displacements were used to attack µo. Quite frequently, a combination of these mechanisms has been applied, alkaline/surfactant/ polymer being a good example. Similarly, to address improvement in sweep, blocking agents such as polymers, microgels, or microbes, or a combination of a couple of these mechanisms, have been applied. While the underlying mechanisms remain the same, major developments in EOR in recent years center on the use of engineered water or nanoparticles to reduce Kw, on the experimental development of chemicals that are less susceptible to high-temperature and high-salinity environments, or on the use of novel computational techniques and machine learning to design and monitor the flood operations better. The literature of the last year is an important reflection of that evolution, as the selected papers reflect. Recommended additional reading at OnePetro: www.onepetro.org. SPE 195553 Investigation of Pore-Scale Mechanisms of Microbial Enhanced Oil Recovery Using Microfluidics by Calvin Gaol, Clausthal University of Technology, et al. SPE 192110 Polymers and Their Limits in Temperature, Salinity, and Hardness: Theory and Practice by Eric Delamaide, IFP Technologies Canada, et al. SPE 192651 Performance Evaluation and Field Trial of Self-Adaptive Microgel Flooding Technology by Zhe Sun, China National Offshore Oil Corporation, et al.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.092 |
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".