A compact high-performance low-field NMR apparatus for measurements on fluids at very high pressures and temperatures
Bibliographic record
Abstract
We discuss an innovative new high-performance apparatus for performing low-field Nuclear Magnetic Resonance (NMR) relaxation times and diffusion measurements on fluids at very high pressures and high temperatures. The apparatus sensor design and electronics specifications allow for dual deployment either in a fluid sampling well logging tool or in a laboratory. The sensor and electronics were designed to function in both environments. This paper discusses the use of the apparatus in a laboratory environment. The operating temperature and pressure limits, and the signal-to-noise ratio (SNR) of the new system exceed by a very wide margin what is currently possible. This major breakthrough was made possible by a revolutionary new sensor design that breaks many of the rules of conventional high pressure NMR sensor design. A metallic sample holder capable of operating at high pressures and temperatures is provided to contain the fluid under study. The sample holder has been successfully tested for operation up to 36 Kpsi. A solenoid coil wound on a slotted titanium frame sits inside the metallic sample holder and serves as an antenna to transmit RF pulses and receive NMR signals. The metal sample holder is sandwiched between a pair of gradient coils which provide a linear field gradient for pulsed field gradient diffusion measurements. The assembly sits in the bore of a low-gradient permanent magnet. The system can operate over a wide frequency range without the need for tuning the antenna to the Larmor frequency. The SNR measured on a water sample at room temperature is more than 15 times greater than that of the commercial low-field system in our laboratory. Thus, the new system provides for data acquisition more than 200 times faster than was previously possible. Laboratory NMR measurements of relaxations times and diffusion coefficients performed at pressures up to 25 Kpsi and at temperatures up to 175 °C with crude oils enlivened with dissolved hydrocarbon gases (referred to as "live oils") are shown. This is the first time low-field NMR measurements have been performed at such high temperatures and pressures on live crude oil samples. We discuss the details of the apparatus design, tuning, calibration, and operation. NMR data acquired at multiple temperatures and pressures on a live oil sample are discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".