Advances in Heavy Oil and Water Property Measurements Using Low Field Nuclear Magnetic Resonance
Bibliographic record
Abstract
Abstract Low field NMR of fluids can be used to measure physical properties of water and oil such as viscosity and diffusion coefficients. Remarkably accurate measurements can be obtained from simple and fast measurements in a beaker. Algorithms for the determination of heavy oil and bitumen viscosity have been previously developed that can provide first order estimates over a variety of viscosity ranges (100-107 mPas) covering variable temperatures, water/oil ratios and oil compositions. When the algorithms are tuned for single oils, then the accuracy increases dramatically and the predictions are as accurate as direct viscosity measurements. In this paper, the aforementioned algorithms are extended to predict NMR response and viscosity predictions for live vs. dead heavy oil samples, and virgin vs. solvent-diluted heavy oil samples. The viscosity predictions of oils in beakers are compared to the predictions of the same oils while in reservoir conditions (i.e. in-situ). The proposed algorithms can be used in reservoir characterization and on-line viscosity measurements in heavy oil reservoirs. A NMR based water cut meter was recently introduced for accurate measurement of oil and water cut values. The instrument appears to be superior to conventional measurement devices since it does not seem to be affected by salinity, emulsion characteristics or temperature to date. Extensive field measurements have proved the above claims. The principles of this water cut device are further extended to the measurement of water cut oil cut and gas cut under laboratory conditions. Mixtures of heavy oil and bitumen with water and air were prepared in the laboratory and their NMR characteristics were identified under a broad range of saturations. The results were compared against mass balance measurements. It is demonstrated that the two-phase measurement algorithms can be extended to three phase systems. Thus the first step towards accurate multi-phase measurements can be achieved.
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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) | 0.002 | 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".