Frequency Dependent Magnetic Resonance Response of Heavy Crude Oils: Methods and Applications
Bibliographic record
Abstract
Abstract Oilfield nuclear magnetic resonance (NMR) applications are widely accepted for characterizing reservoir rocks and fluids. All of the downhole applications, and most oilfield NMR lab work, are carried out assuming that the results are independent of the operating frequency. The assumption is generally warranted, since most NMR logging tools and lab devices operate in the 0.5 to 2 MHz range. However, two strong motivations exist for investigating the frequency dependence (that is, dispersion) of NMR of crude oil samples: 1) introduction and acceptance of lower frequency logging while drilling (LWD) and multi- frequency wireline NMR tools, and 2) sensitivity of NMR dispersion to the interaction and dynamics of molecules of varying size in complex fluids. We report here on a versatile frequency-dependent lab NMR measurement known as fast field cycling (FFC) NMR. The results clearly demonstrate a frequency dependence of the longitudinal relaxation time, T1, for crude oils between 10 kHz and 40 MHz. The study investigates the full T1 distributions for crude oils containing significant amounts of all the SARA (saturates, aromatics, resins, asphaltene) fractions, including a broad range of concentrations for the heavier fractions. For crude oils containing minimal asphaltene and resin fractions, the dispersion is minimal. In contrast, crude oils containing larger concentrations of asphaltene and resins show a clear shift of the T1 distribution to longer times at higher frequencies. We will discuss the implications and benefits of NMR dispersion for oilfield application. We suggest how the dispersion can be understood in terms of the molecular dynamics of asphaltenes with the rest the oil. Finally, we will provide an overview of the experimental challenges in making these measurements, including the hardware design and the specialized pulse sequences required for acquiring multi-frequency data.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".