Recovery Improvement of Gravity Driven Steam Applications Using New Generation Chemical Additives
Bibliographic record
Abstract
Abstract Reservoirs containing very heavy oil or extremely heterogeneous/fractured geology are not convenient for steamflooding and even cyclic steam injection. Then, steam can be used to heat the reservoir and accelerate the recovery by gravity drainage. Two well-known applications of this method are steam assisted gravity drainage (SAGD) and thermally assisted gas oil gravity drainage. Although the latter is not commercially applied, the former is a proven technology with remarkable production in Canada and Venezuela. Due to the risks caused by the cost and solvent retention, no large scale applications of solvent injection with steam have been implemented. An alternative is to use chemicals as suggested a few decades ago to alter the interfacial forces and improve microscopic displacement. This paper presents experimental results on testing -new generation- chemicals for their capability in recovery improvement. Sandpack experiments were conducted to evaluate the incremental in oil recovery by chemical additives compared to sole steam injection. Steam and chemicals were heated and introduced to the system from separate channels at the entrance of the vertically situated sandpack (30 cm long, 5 cm in diameter). To generate a purely gravity dominated system (pressure differential of 10–25 psi) a back pressure regulator was used. The chemicals used include thermally stable surface agents, such as surfactants (AAS J1111, O352, LTS-18), Tween 20, biodiesel), ionic liquid (BMMIM BF4), high pH solution (NaBO2), solvent (heptane), and nanoparticles (SiO2). The oil selected was 20,000 cp crude. Incremental recoveries were monitored and related to the thermal stability of the chemicals. A comparative analysis was provided as to their contribution to the reduction of the cost (less steam and lower temperature) and chemicals were classified based on their recovery improvement performance and thermal stability. Through this experimental schematic, the highest increment in oil recovery was achieved by LTS-18 but also combined a high duration of the experiment with a high water consumption. This reduces the result in economical favorable conditions of the LTS-18. Biodiesel had the best performances in steam-to-oil ratio (SOR) and its effects needs to be further investigated. Tertiary injection of hot water with a surfactant was inefficient. Ionic liquid increased the oil recovery in the tertiary stage after the core was flooded with a low quality steam by 20%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".