Micropore network modelling from 2D confocal imagery: impact on reservoir quality and hydrocarbon recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microporosity in carbonate reservoirs is globally pervasive and commonly used to explain high-porosity, low-permeability reservoirs, higher than expected water saturations, low resistivity pay zones and poor sweep efficiency. The potential for micropores to store and produce hydrocarbons has long been recognized, yet limitations on tools to evaluate microporosity has prevented rigorous evaluation. Here we demonstrate a workflow for evaluating microporosity through a combination of laser scanning confocal microscopy (LSCM) and pore network modelling. Specific values for microporosity and permeability calculated in our study should not be applied explicitly, as these are simulated values, but they demonstrate the viability of micropore networks to store and flow hydrocarbons. Carbonate reservoir assessment is critical not only in the petroleum industry, but also for applications in hydrothermal and mineral resources, carbon capture and storage, and groundwater supply. This approach can be applied to understand the potential for any reservoir to hold and transmit fluids.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it