Detection and Reuse of the Produced Chemicals in Alkaline-Surfactant-Polymer Floods
Bibliographic record
Abstract
Abstract Alkaline-Surfactant-Polymer (ASP) flood processes have been increasingly applied in the oil fields due to their high ultimate oil recovery. However, a major technical challenge is how to significantly reduce the amount and the cost of chemicals used such that ASP floods can become cost-effective as well. On the other hand, field applications show that the concentrations of alkali, surfactant and polymer remain relatively high in the produced fluids of ASP floods. Thus, successful detection and reuse of these chemicals can substantially reduce the capital cost and the environmental impact. In this paper, several methods are applied to detect each chemical and quantify its concentration in the produced fluids. Also re-injection of the produced chemicals is conducted for further enhancing oil recovery. More specifically, first, the total interactions of each chemical with the oil-brine-rock system are studied. With the developed detection techniques for each individual chemical used in ASP floods, the total loss of each chemical is measured. The chemical loss is caused by its chemical reactions with the crude oil and the reservoir brine, as well as its adsorption onto the rock surface. Secondly, coreflood tests are performed for alkaline floods, surfactant floods, alkaline-surfactant (AS) floods, and ASP floods to determine their respective tertiary oil recovery. Hence, a better understanding of how each chemical contributes to enhanced oil recovery (EOR) is achieved. Thirdly, typical chemical concentrations in the produced fluids are measured and compared with those in the injected slugs so as to determine the potential of reusing these chemicals in practice. The follow-up coreflood tests of reusing these produced chemicals are carried out by directly re-injecting the produced fluids. The test results show that the produced chemicals can still effectively enhance oil recovery by up to 19.7%. It is anticipated that the detection and reuse techniques studied in this work should facilitate the design, optimization and implementation of ASP flood 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".