Bibliographic record
Abstract
Abstract SAGD (steam assisted gravity drainage) has been proven as an effective technology to enhance heavy oil/bitumen recovery. The main shortcoming of this method is its inefficiency, a result of high water and energy consumption. As a solution to SAGD efficiency improvement, we propose the addition of chemicals resulting in higher recovery and reduced steam consumption. The objective of this paper is to screen new generation chemicals as additives and study the mechanisms and optimum injection strategies. This screening was achieved through Hele-Shaw type macroscopic visual experiments. We previously screened a wide variety of chemical additives (Bruns and Babadagli 2017, 2018) for steam flooding. As a continuation of this work, these chemicals were tested for SAGD conditions using a new visual experimental design where the optimal injection strategies were identified, eventually providing a reference for the selection of chemical additives for field applications. 11 conventional and new generation chemical additives (heptane, biodiesel, DME, LTS-18, Tween 80, Span 80, Novelfroth 190, ionic liquid (BMMMIM BF4), silicon dioxide nanoparticle, DES9, and DES11) were selected based on both their strong thermal stability and enhanced oil recovery capability. The recovery improvement mechanisms for the different chemical additives and different injection strategies were identified through flow characteristics, emulsifying ability, viscosity reduction capability, and wettability alteration. Simultaneously, the mechanisms were studied from a macro perspective via analyzing areal sweep efficiency and microscopic oil displacement efficiency together with observing the images acquired during the process. Three different injection strategies were applied for each chemical: (1) chemicals were injected at the beginning, (2) in the middle, and (3) at the end of the steam injection. The chemical additives played different roles in recovery improvement, and different chemical addition strategies yielded different mechanisms. Heptane exhibited extraordinary characteristics with maximum "steam saving" (34.52%) when the middle injection strategy was applied and maximum ultimate oil recovery (64.75%) was obtained for the end injection strategy due to the ability to reduce the viscosity of heavy oil by dissolving around the chamber edge. Steamflooding with Novelfroth 190 showed an excellent performance for the middle and end injection strategies due to its ability to develop rapid oil drainage "channels". The addition of surfactant LST-18 presented the ability to improve the EOR by forming emulsions. Additionally, the distributions of the steam chamber in the Hele-Shaw cell were different due to the changed flow characteristics when the same chemical additive was injected at different times, thus showing the ability to reduce viscosity and form emulsions with different strengths.
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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 | 0.001 | 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) | 0.002 | 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".