Evaluating Brine Deposits Using Borehole Magnetic Resonance
Bibliographic record
Abstract
Brine mining is an important source of elements such as potassium, iodine, lithium, and bromine that occur in solution in groundwater, typically in shallow brines occurring beneath saline or dry lakes or in deep brines in sedimentary basins. Where feasible, brine mining is an attractive alternative to conventional mining due to lower surface and environmental impact and lower OPEX than conventional mining operations.As with any resource, evaluating brine deposits requires developing an understanding of how much resource is present and how it can be most economically produced. How much resource is present is a function of the bulk aquifer volume, the specific yield, and the brine composition, while the primary subsurface control on economic production is hydraulic conductivity, which dictates the rate at which the brine can be produced to surface. Specific yield and hydraulic conductivity are analogous to the free fluid volume and permeability quantities that are of interest in oil and gas resource assessment.Borehole magnetic resonance has been applied in the oil and gas industry for the evaluation of bound and free fluid volumes and permeability for over twenty years. These same methodologies are equally applicable in the evaluation of brine deposits, however the hypersaline brines that are targets for commercial development cause highly conductive borehole environments that can be extremely challenging for magnetic resonance measurements. Nevertheless, use of borehole magnetic resonance measurements to help evaluate a sulphate of potash brine deposit currently under assessment shows that such measurements can be employed successfully in these environments.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".