A New Method for Group Analysis of Petroleum Fractions in Unconsolidated Porous Media
Bibliographic record
Abstract
Abstract A fast and accurate method for the group analysis of crude oils in porous media that describes petroleum components (especially heavy fractions) has been developed. NMR structure group analysis is used as the tool for the characterization of crude oils. This method is proposed as an alternative to existing complicated and laborious methods of characterization of extracted oil samples. Currently, group analysis of heavy fractions of crude oils is being investigated by means of chromatographic methods, such as the SARA (saturates-aromatics-resinsasphaltenes) test. These methods are usually expensive, and require considerable work from qualified personnel. Furthermore, these methods cannot be used for estimating the oil components in situ. The basis of determining crude oil components using NMR is the difference of the nuclei mobility in the different hydrocarbons during the NMR testing period. A combination of solvent extraction, NMR testing and data processing gives a series of NMR terms that are then used to specify hydrocarbon mixtures and their components both in the bulk phase and in unconsolidated porous media. Introduction NMR logging tools are currently used for determining reservoir properties such as porosity(1, 2), permeability(1-4), as well as mobile and immobile fluids(5-7). Recent developments in NMR research offer tools for separating water, oil, and gas from the combined NMR signal(7, 8). Very little is known about the use of NMR logging tools for the in situ characterization of crude oils(1). With respect to heavy oil and bitumen formations, NMR logging has not been very successful in characterizing crude oil. The reason for the lack of such success is the fact that the NMR logging tools cannot detect the spectra from most heavy oil and bitumen formations. It should be noted that high field NMR technology has solved such problems in the past, but such technology cannot be used downhole. A fundamental objective of the research performed in our laboratory is to extend the use of NMR logging tools to all heavy oil and bitumen formations. To this end, the NMR characteristics of heavy oils in porous media were investigated(9, 10). The objective of our work is to isolate the oil signal from the combined NMR spectrum of the formation, and then shift it towards the relaxation time range that can be detected by the conventional NMR logging tools. Once this goal is achieved, NMR logs of heavy oil and bitumen formations can become successful in the analysis of oil downhole. This objective was accomplished in the material presented in this paper through a series of experiments that addressed the following issues:Relaxation interactions in mixtures of simple organic liquids with water.Relaxation interactions in mixtures of simple organic liquids. Relaxation interactions in mixtures of simple organic liquids with solvents.Relaxation times of conventional crude oils with and without solvents present.Relaxation times of heavy crude oils and bitumen with and without solvents present.Relaxation times of heavy crude oils and bitumen with and without solvents in unconsolidated sands and with variable connate water saturation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".