Optimal Application Conditions of Solvent Injection into Oilsands to Minimize the Effect of Asphaltene Deposition: An Experimental Investigation
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Bibliographic record
Abstract
Abstract Solvent injection into heavy-oil reservoirs is quite complex on account of the asphaltene destabilization that occurs due to the changes in temperature, pressure, and solvent type dissolved in oil. As a result of this destabilization, the asphaltene precipitates, flocculates, and eventually plugs the pores in the reservoir due to the formation of asphaltene clusters. In solvent applications, light molecular weight hydrocarbon solvents are preferred because of their high diffusion coefficient; but, 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) injection into sand pack systems saturated with heavy-oil (87651 cp) was evaluated at different pressure conditions that are applicable to typical Canadian oilsands reservoirs (100-300 psi) and temperatures (25-120 °C). First, the asphaltene behavior of different solvents at different pressures and temperatures were studied through deasphalting work in a pressure-volume-temperature (PVT) cell. Based on the quantitative (amount of asphaltene precipitated) and qualitative (microscopic images of asphaltene clusters) observations, the characteristics of asphaltene were classified in terms of their shape, size, and amount for different oil/solvent types, pressure, and temperature. Continually, the same n-alkane solvents and heavy-oil were used in gravity drainage recovery experiments on unconsolidated sands. 2-D visual (Hele-Shaw type) and 3-D (cylindrical) sand pack experiments were carried out at the same temperatures and pressure conditions used for the PVT experiments. The asphaltene precipitated in the sand pack and in the produced oil was collected, and the standard 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 the visual inspection of 3-D sand pack experiments and the 2-D visual model using microscopic visualization and cross-checked against oil recovery rate. The asphaltene characteristics and concentration were evaluated using the microscope visualization and refractive index values, respectively. Eventually, optimal application conditions for solvent and thermally-aided solvent injection were listed for a wide range of heavy-oil and solvent types.
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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 it