Bibliographic record
Abstract
Abstract Monitoring fluid movements and associated saturation changes over time is a key feedback element for enabling optimized reservoir management. Reservoir-wide inquiries made by time-lapse seismic acquisitions, though possible, are rare even in the world's biggest fields, and generally do not provide sufficient resolution or repeatability, nor a direct assessment of saturation changes. Conversely, conventional logging methods that do have high resolution and may provide the desired precision in saturation (e.g., pulse neutron logging and through casing resistivity methods), but do so on a localized basis, suffering from very limited depths of investigation. Thus, there exists a data gap, or opportunity, for geophysical techniques exhibiting moderate to large depths of investigation, with moderate or better resolution. Time-lapsed gravity logging is one such method that if, given a significant "technology overhaul," could eventually close this gap. Subsurface measurements (versus those collected using various surface meters) benefit from enhanced signal-to-noise ratios by virtue of their immediate proximity to saturation contrasts and changes over time. Both practical results and analytic studies reported in literature establish gravity's intrinsic large depth of investigation. Furthermore, data relate directly and easily through bulk density to changes in fluid saturations. The notion of reservoir monitoring via gravity logging is not new; however, measurement concepts and technology applied in emerging sensor development programs are. This brief synopsis gives insight to pursuits down one technology path – the BHg slim logging tool overviewed here aims to provide an order of magnitude improved performance compared to its precursor borehole gravity meter (BHGM), free of restrictions on well deviation and without a need to pull tubing. The translation of gravity-centric tool performance (i.e., micro-Gal units) to g/cm3 density contrasts and component saturations requires specific problem geometry and reservoir properties, but is invaluable for providing a tangible illustration of tool capabilities to non-gravity practitioners.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".