Estimation of Solubility and Diffusivity of Gases in Heavy Oils by Use of Late-Time Pressure-Decay Data: An Analytical/Graphical Approach
Bibliographic record
Abstract
Summary Solubility and diffusivity of gases in heavy oils, quantified by Henry's constant (Hij) and the diffusion coefficient (D), respectively, are essential properties for the design of recovery processes that require the injection of gas or vapor solvents into the reservoir. Data, obtained from various experimental procedures such as the pressure-decay technique (PDT), are used to estimate these two parameters. The PDT uses a pressure/volume/temperature (PVT) cell where the gas-phase pressure declines as gas diffuses into heavy oil following an early- and a late-time regime. Current approaches to analyze data from the conventional PDT are either graphical techniques based on early-time data or full numerical simulation. Early-time data, when the diffusing gas has not reached the bottom of the PVT cell, do not provide enough information to simultaneously estimate both the diffusion coefficient and Henry's constant. Hence, existing graphical procedures are limited to diffusion-coefficient estimation. In this paper, we propose a novel graphical technique to estimate the diffusion coefficient and Henry's constant by use of the late-time data from pressure-decay experiments. Our method is derived from the modeling of gas-phase pressure decay by use of Fick's second law and gas-phase mass-balance equations. We use the integral method to provide an approximate analytical solution to the set of equations. In addition, by use of the resultant solution, we develop a simple graphical method to directly estimate both the diffusion coefficient and Henry's constant. The estimated parameters through the proposed technique for methane/bitumen and carbon dioxide/bitumen experiments are in close agreement with those reported in the literature.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".