A Review on the Use of Chemicals as Steam Additives for Thermal Oil Recovery Applications
Bibliographic record
Abstract
Abstract This study conducts a literature survey on the chemical steam additives tested in both lab and field settings from 1982 to present (2020). We summarize the major recovery mechanisms of both steam-based recovery process and steam-chemical-based recovery process. Next, we review the previous lab-scale/field-scale studies examining the applications of surfactants, alkali, and novel chemicals in the steam-based oil recovery process. Among the different surfactants studied, alpha-olefin sulfonate (AOS) and linear toluene sulfonate (LTS) are the recommended chemicals for their foam control/detergency effect. In particular, AOS was observed to perform especially well in residual oil saturation (ROS) reduction and sweep efficiency improvement when being co-injected with alkali. Application of organic alkali (alone or with a co-surfactant) has also drawn wide attention recently, but its efficacy in the field requires further investigation and the consumption of alkali by sands/clay is often an inevitable issue and, therefore, how to control the alkali loss requires further investigation. Novel chemical additives tested in the past five years include fatty acids (such as tail oil acid, TOA-Na+), Biodiesel (o/w emulsion), along with other types of chemical additives including switchable hydrophilicity tertiary amines (SHTA), chelating agents, Deep Eutectic Solvents (DES), graphite and SiO2 particles, ionic liquids and urea. High thermal stability of some of the novel chemicals and their potential in increasing displacement efficiency and ROS reduction efficiency in the lab studies require further investigation for their optimized application in the field settings to minimize the use of steam while improving the recovery effectively. This review reveals that when being properly applied, chemical additives can improve oil recovery via steam foam control, detergency effect (IFT reduction and wettability control), and viscosity reduction. In certain cases, microemulsion generation could be observed (o/w or w/o) with the addition of chemical additives at steam condition (which leads to recovery improvement), but the microemulsion effect on the conformance control (separate from the foamy effect), is lacking detailed investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".