Determination of Dispersion Coefficient of Solvent in Heavy Oil/Bitumen Under Reservoir Conditions
Bibliographic record
Abstract
Abstract In this work, the dispersion coefficient of solvent in heavy oil/bitumen is innovatively determined by employing an inverse approach to accurately reproduce the measured temperature, pressure, and production profiles in a heated vapour extraction (H-VAPEX) process, thereby significantly advancing our insights into the dispersion physics under reservoir conditions. Monitoring and surveillance data, including temperature profiles, solvent injection rates, and fluid production rates, from a large three-dimensional (3D) physical experiment for an H-VAPEX test have been meticulously collected, analyzed, and processed. More specifically, the evolving geometries of the solvent chamber have been precisely delineated by integrating the advection-dispersion equation (ADE) with a modified Peng-Robinson equation of state (PR EOS), where the solvent chamber interface (SCI) is theoretically formulated as a function of temperature and solvent concentration gradient. As dispersion physics significantly impacts the SCI advancement, the dispersion coefficient in the presence of porous media can be inversely determined by assimilating the measured and calculated morphological contours of the solvent chamber. Such an integrated model has been rigorously validated with the measured temperature profile of a steam-assisted gravity drainage (SAGD) process from a one-dimensional (1D) physical model collected from literature, and subsequently extended to a large-scale 3D application. In addition, sensitivity analysis has been performed to analyze and identify the key parameters dominating the H-VAPEX performance. Not only does the injection velocity of solvent and its concentration gradient at the SCI exert a significant influence on its anisotropic dispersion in various spatial dimensions, but also the injection velocity dictates the solvent propagation over time. A higher injection velocity is found to accelerate the expansion of solvent chamber, thereby promoting solvent dispersion and resulting in a more pronounced solvent concentration gradient beyond the SCI. Under reservoir conditions and with high injection velocities, the dispersion coefficient is found to substantially exceed the diffusion coefficient by several orders of magnitude. Nevertheless, a large reduction in the viscosity of heavy oil/bitumen saturated with solvent results in a great increase in fluid mobility, facilitating the solvent injection as well as SCI propagation. Through the repeatable and consistent H-VAPEX experiments within the large 3D physical model, this systematic and robust method enables us, for the first time, to not only inversely determine the dispersion coefficient of solvent under reservoir conditions, but also to accurately evaluate and optimize the growth and propagation dynamics of the solvent chamber.
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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) | 0.000 | 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 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".