Addressing the Clay/Heavy Oil Interaction When Interpreting Low Field Nuclear Magnetic Resonance Logs
Bibliographic record
Abstract
Abstract Low field nuclear magnetic resonance (NMR) in the form of logging tools, bench top analysers or on-line sensors has shown tremendous promise for the interpretation of logs and subsequent characterization of heavy oil and bitumen reser-voirs as well as the evaluation of recovery efficiency. The key for this success is the ability to identify properly the NMR response of the heavy oil and bitumen components of the spectra. Contrary to earlier belief on this topic, NMR can de-tect at least part of the response of heavy oil and bitumen ei-ther down-hole or in the production line. The majority of the spectra for heavy oil and bitumen samples are detected at re-laxation times less than 10 ms. In clay free sands this spec- trum is quite clear and the response of the NMR interpretation algorithms is accurate. However, the spectra of sand contain-ing clays show clay bound water to be in the same range as bitumen. Not properly accounting for the contribution of the clay bound water in NMR spectra results in the overestimation of oil or bitumen content of a given formation. This paper presents experimental results of heavy oil samples and also the spectra of clay bound water for common clays found in Al- berta and Saskatchewan. After the spectra are compared inde-pendently, sand packs containing different amounts of clay and bitumen are prepared and their NMR spectra are obtained. Patterns of spectra overlapping are identified and a prelimi-nary clay bound water prediction algorithm is presented. This algorithm allows for the separation of the oil contribution from the total NMR spectrum. Examples from different formations illustrate the process by which NMR can be used to determine the oil saturation in different Alberta formations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".