Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169071, ’CO2 Low-Salinity Water Alternating Gas: A New Promising Approach for Enhanced Oil Recovery,’ by Cuong T.Q. Dang, SPE, University of Calgary; Long X. Nghiem, SPE, Computer Modelling Group; Zhangxin Chen and Ngoc T.B. Nguyen, SPE, University of Calgary; and Quoc P. Nguyen, SPE, The University of Texas at Austin, prepared for the 2014 SPE Improved Oil Recovery Symposium, Tulsa, 12-16 April. The paper has not been peer reviewed. Significant advantages have been seen from combining low-salinity waterflooding (LSW) with other enhanced-oil-recovery (EOR) techniques. This paper proposes a novel concept of low-salinity-water-alternating- gas (LSWAG) injection with CO2 under CO2-miscible-displacement conditions. While LSW is an emerging EOR method based on alteration of wettability from oil-wet to water-wet conditions, water-alternating-gas (WAG) injection is a proven method for improving gas-flooding performance by controlling gas mobility. Therefore, LSWAG injection promotes a synergy of the mechanisms underlying these methods that enhances oil recovery further. Introduction LSW is receiving increasing attention in the oil industry and is currently identified as an important EOR technique because it shows more advantages than conventional chemical EOR methods in terms of chemical costs, environmental impact, and field process implementation. Although the benefits of LSW have been realized, the mechanism for incremental oil recovery by LSW is still a topic that is open for discussion. Among the proposed hypotheses, wettability alteration toward increased water-wetness during LSW is accepted widely as the cause for the EOR. It has been found experimentally that low-salinity brine has a significant effect on the shape and the endpoints of the relative permeability curves, resulting in a lower water relative permeability and higher oil relative permeability. The mechanisms of wettability alteration because of ion exchange and geochemical reactions have been implemented successfully in a compositional simulator for modeling LSW. Excellent agreements between simulation results and important measurements from coreflood experiments and pilot observations were obtained with this modeling approach.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".