Recovery Improvement by Chemical Additives to Steam Injection: Identifying Underlying Mechanisms Through Core and Visual Experiments
Bibliographic record
Abstract
Abstract Steam injection of any kind (flooding, cyclic, or gravity drainage) is a proven heavy-oil recovery method; however, it also involves excessive costs due energy and water needed for steam generation. Any effort in reducing this cost or improving oil recovery is essential for sustainable production, especially in times of low oil prices. Chemical additives to steam were suggested a few decades ago to improve two major mechanisms, namely heat transfer and interfacial phenomena, but research in that area discontinued due to the cost and thermal stability problem of the additive chemicals. With recent advancements in nano-technologies, new generation chemicals showed potential to reconsider chemical additives to improve the efficiency of steam injection. This, however, requires extensive research especially for mechanism identification. The objective of this paper is to identify the flow characteristics and the mechanisms involved in recovery enhancement by chemical additives through core and visual tests. To mimic the gravity assisted drainage and flooding type steam displacement tests we performed previously (Bruns and Babadagli 2017) on cores saturated with 27,000 heavy-crude-oil, a visual Hele-Shaw model was designed to simulate the same process and identify the physical characteristics of the steam-condensate-oil interface and the role played by added chemicals. Majority of the chemicals/chemical blends showed either improvement in the rate or ultimate recoveries in the coreflooding tests and, based on this data, the best performing and the most thermally stable chemicals were selected for the visual tests. These chemicals include ionic liquids, internal olefin sulfonate, biodiesel (thermally stable surface active agents) and solvents (heptane), and nano-fluids (silicon oxide). The chemical solution was injected at constant rate and pressure after being vaporized in an oven along with steam and the whole process was recorded with a camera. The contribution to recovery improvement through these phenomena in flooding and gravity controlled cases were identified. Foaming, emulsification, and IFT reduction yielding reduced drag forces between two phases at the interface were observed to be the main reason for positive contribution of chemicals. Biodiesel (Surfactant 1) exhibited a diffusion-like behavior near the injection port where no residual oil was noticed. The solvent (heptane), simulating ES-SAGD, stabilized the flow of steam in the late stage of the experiment due to the viscosity reduction. Improved oil + condensate drainage was assumed to be the contributing mechanism because of the change in surface properties during the injection of the ionic liquid. Nanoparticle, silicon oxide, and the internal olefin sulfonate (Surfactant 2) showed similar improvements in tip-splitting of the displacing fingers. It was concluded that the interfacial tension (IFT) reduction resulted in a wider occupation of the Hele-Shaw cell (better lateral sweep).
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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".