Optimal Application Conditions of Solvent Injection Into Oil Sands To Minimize the Effect of Asphaltene Deposition: An Experimental Investigation
Bibliographic record
Abstract
Summary Solvent injection into heavy-oil reservoirs is quite complex because of the asphaltene destabilization that occurs because of the changes in temperature, pressure, and solvent type dissolved in oil. As a result of this destabilization, the asphaltene flocculates, agglomerates, and eventually plugs the pores in the reservoir because of the formation of asphaltene clusters. In solvent applications, light-molecular-weight hydrocarbon solvents are preferred because of their high diffusion coefficient; however, as the carbon number of n-alkane solvents decreases, asphaltene precipitation increases. Therefore, the selection of the solvent and application condition is highly critical in cold and thermally aided solvent applications. In this research, low-carbon-number n-alkane (propane, n-hexane, and n-decane) and a distillate-hydrocarbon (obtained from a heavy-oil-upgrading facility) injection into glass-bead-pack systems saturated with heavy oil (87,651 and 20,918 cp at 25°C) were evaluated at different pressure conditions that are applicable to typical Canadian oil-sand reservoirs (698–2068 kPa) and temperatures (25–120°C). First, the asphaltene behavior of different solvents at different pressures and temperatures was studied through deasphalting work in a pressure/volume/temperature (PVT) cell in previous work [Moreno and Babadagli (2013)]. By use of quantitative (amount of asphaltene precipitated) and qualitative (microscopic images of asphaltene clusters) observations, asphaltenes were classified in terms of their shape, size, and quantity for different oil/solvent types, pressure, and temperature. Continually, the same n-alkane, distillate-hydrocarbon solvents, and heavy oil were used in gravity-stable-displacement glass-bead-pack experiments. 3-D (cylindrical) glass-bead-pack experiments were carried out at the same temperature and pressure conditions used for the PVT experiments. The operational conditions, oil composition, and solvent type showed significant effects on oil-recovery factor. Asphaltene deposition and residual oil saturation (ROS) in the glass-bead pack and the amount of asphaltene in the produced oil were measured, and the standard saturate, aromatic, resin, and asphaltene (SARA) analysis was applied to determine the optimal operating conditions yielding the highest recoveries with minimal pore plugging. Moreover, the pore-plugging process was analyzed through a visual scanning electron microscope (SEM) and optical microscope to find the different organic deposition formation and agglomeration. Oil production was evaluated by use of microscope visualization, viscosity reduction, and refractive-index values. Eventually, optimal application conditions for solvent and thermally aided solvent injection were listed for a wide range of heavy-oil and solvent types.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".