Estimation of Diffusion Coefficients in Bitumen Solvent Mixtures Using X-Ray CAT Scanning and Low Field NMR
Bibliographic record
Abstract
Abstract Diffusion plays an important role in both VAPEX and Solvent Injection processes for oil/bitumen recovery. In this paper, X-ray CAT scanning and low field NMR have been used to obtain experimental data for the diffusion of several solvents in bulk bitumen. Low field NMR has been used successfully in determining both solvent content and viscosity reduction in heavy oil and bitumen mixtures with various solvents. NMR is also a potential tool for diffusion coefficient measurement in solventbitumen system (1). In this paper, the results of diffusion coefficient calculated from NMR is further discussed and compared with the results obtained from X-ray CAT scanning. X-ray CAT scanning takes advantage of density contrasts in the scanned sample through the measured CT number. With solvent diffusing into heavy oil or bitumen, the CT number changes during the process and provides the corresponded density of the mixture. Therefore, the concentration gradient distribution with distance can be obtained. A Fick type of equation can be written and an apparent diffusion coefficient can be calculated. This approach and the results of this analysis are presented along with an evaluation of the applicability of the assumptions in Fick's Law. Introduction With more attention for the recovery of heavy oil and bitumen in Alberta based on solvent processes, mass transfer between solvent and bitumen become an important process to be understood. However, only a few experimental values of the diffusion coefficient of various organic substances into bitumen are available in the open literature(2–5). To understand better the mass transfer phenomena, more experimental data are necessary, especially in liquid-liquid systems. This paper presents further research results to previous work(1). Traditionally an optical system is used to record time dependent patterns that can be photographed and then analyzed to yield either binary or ternary diffusion coefficients for the system of interest(6). The work presented here makes use of Xray CAT scanning and low field NMR as the tools for mixing pattern recording. Low field NMR has great potential as a tool for measuring properties of reservoir fluids and produced liquid streams(7). From a single NMR measurement of a fluid stream containing oil and water, the relative fractions of both liquids can be determined(8). As a solvent comes into contact with a heavy oil or bitumen sample, then the mobility of hydrogen bearing molecules of both solvent and oil change. These changes are detectable through changes in the NMR relaxation characteristics of both solvent and oil and can be correlated to mass flux and concentration changes. Fick's second law of diffusion is used to model such mass flux and consequent concentration changes, and an apparent diffusion coefficient can be calculated. This approach was presented in an earlier paper(1). In the present paper, this method is further discussed. Computer-Assisted Tomography (CAT) scanning using Xrays is also becoming an attractive tool for petroleum engineers. The method can give an image of a core in two or three dimensions with a very fine resolution and high accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".