Estimation of Diffusion Coefficients in Bitumen Solvent Mixtures as Derived From Low Field NMR Spectra
Bibliographic record
Abstract
Abstract Use of solvents for the extraction of heavy oil and bitumen appears to be an increasingly feasible technology. Both Vapour Extraction and direct Solvent Injection are considered for conventional exploration and production schemes, while solvent dilution of bitumen is a standard technique in oil sands mining. Mass transfer between solvent and bitumen is a poorly understood process. In some cases it is totally ignored compared to viscous force effects. In some other cases, phenomenological estimations of diffusion and dispersion coefficients are used. Low field NMR has been used successfully in determining both solvent content and viscosity reduction in heavy oil and bitumen mixtures with various solvents. 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 changes. These changes are detectable through changes in the NMR relaxation characteristics of both solvent and oil. These changes can be correlated to mass flux and concentration changes. Based on Fick's second law, a diffusion coefficient, which is independent of concentration, was calculated against three oils and six solvents. Introduction There are abounding formations of heavy oil and bitumen in Alberta. These fields are primary candidates for thermal and solvent based processes for recovery of the oil and bitumen. Both Vapour Extraction and direct Solvent Injection are considered for in-situ exploration and production schemes, while solvent dilution of bitumen is a standard technique in oil sands extraction as part of the secondary froth flotation. Mass transfer between solvent and bitumen is a poorly understood process. Only a few experimental values of the diffusion coefficient of various organic substances into bitumen are available in the open literature (Hayes and Park(1), Oballa and Butler (2), Das and Butler (3), Fisher et al.(4)). To understand better the mass transfer phenomena, more experimental data are necessary, especially in liquidliquid systems. In this paper, such experimental data are obtained for three oils and six solvents. Most methods for studying mass transfer between two liquids employ some type of an optical system to record time dependent patterns that can be photographed and then analyzed to yield either the binary or the ternary diffusion coefficients for the system of interest (5). The work presented in this paper is a deviation from such methods. In the presented work, low field nuclear magnetic resonance (NMR) was used as the tool for mixing pattern recording. Low field NMR has great potential as a tool for measuring properties of reservoir fluids and produced liquid streams (6). From a single NMR measurement of a fluid stream containing oil and water, the relative fractions of both liquids can be determined (7). Low field NMR has been used successfully in determining both solvent content and viscosity reduction in heavy oil and bitumen mixtures with various solvents (8),(9). 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 can be correlated to mass flux and concentration changes.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".