Evaluation of Bitumen-Solvent Properties Using Low Field NMR
Bibliographic record
Abstract
Abstract The VAPEX (vapor extraction) process is a new technique for the recovery of highly viscous heavy oil and bitumen. This process involves injection of vaporized hydrocarbon solvent into heavy oil and bitumen reservoirs and production of the resulting solvent-diluted oil that drains by gravity in a horizontal well. Research has shown that this process is highly efficient and that different solvents give different results. In this paper, six different solvents were added to several oils of different viscosities and densities. The solvents were added in different ratios to each of the oils and NMR spectra were obtained. The mixture of solvent and heavy oil or bitumen produces a spectrum that is distinctly different than that of the solvent or oil alone. From the shape and amplitude of the NMR spectra we can calculate the amount of solvent prevent. Furthermore, we can predict the viscosity of the mixture without any additional viscosity measurements. As asphaltenes precipitate with the addition of solvent we can correlate the amount of asphaltene reduction to changes in the NMR spectra. In this manner, NMR can possibly be used to show the asphaltene precipitation of different oils in the presence of solvent. By measuring the amount of asphaltene precipitation, NMR can also provide an indication of in-situ upgrading of the oil that occurs with the addition of solvent. Using NMR as an analysis tool, the effect of the different solvents on viscosity reduction and asphaltene precipitation is quantified. Introduction The VAPEX process was proposed by Butler and Mokrys(1) for the first time as an alternative to Steam Assisted Gravity Drainage (SAGD) for thin reservoirs where heat lost in the formation would make the process uneconomic. In the VAPEX process vapour solvents, instead of steam, are injected in the reservoir. The solvents dissolve into the bitumen and dramatically reduce its viscosity. The diluted bitumen can drain down to the producer by gravity. Since the original paper, many valuable experimental studies were published using different systems, including Hele-Shaw cells (2)(3)(4), pore network glass micromodels (5), Magnetic Resonance Imaging (MRI) (6) and PVT experiments (7). In this paper, low field Nuclear Magnetic Resonance (NMR) was used to measure the physical properties of heavy oil and bitumen samples with kerosene, hexane, naphtha, haptane, pentane, toluene in several ratios at room temperature and pressure. Low field nuclear resonance (NMR) has vast potential as a tool for measuring properties of a reservoir fluid (8). NMR measurements are simple and non-destructive, but capable of yielding an incredible wealth of information about the reservoir fluid under investigation in a particular sample (9)(10). The mixture viscosity decreases dramatically as the ratio of the injected solvent to heavy oil or bitumen increases, and different solvents show different dilution capacity. The bulk relaxation time of a hydrocarbon fluid is inversely proportional to its viscosity (10). NMR spectra show changes in the response from the mixture after solvent has been added. One goal of this work is to see if one form of viscosity model works for oil-solvent mixtures for different oils mixed with different solvents over a wide range of mixture composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".