Micro-CT Characterization of Pore-size Distribution and Effects on Matrix Acidizing
Bibliographic record
Abstract
Carbonate rocks have complex heterogeneities that result from syn- and post-depositional stressors. These heterogeneities invariably affect the movement of fluid through the formation. When considering an acid treatment procedure, care must be taken to optimize the acid concentration and pumping schedule to encourage the formation of wormholes. Despite the abundance of carbonate formations (60% of conventional reserves), there is little consensus on the effect of physical formation properties related to acidizing efficiency. This study characterizes the pore-size distribution for different carbonate rocks and evaluates how the optimum pore-volume to breakthrough, PV bt, opt, and the optimal interstitial flux, vi, opt, are related to various physical properties of the rock. \n\nThe pore-size distributions evaluated in this study are constructed with micro-computer tomography (micro-CT) imaging, a non-invasive X-Ray imaging technique pioneered in the medical field. Micro-CT is improved over medical CT because it can scan at higher energies and higher resolution. In this work, resolution for scanned samples are from 5-8 µm/voxel and sample sizes are approximately 1cm^3. From the raw data, image processing is applied to distinguish pore space from the surrounding matrix. Object counter software is used to identify and measure individual pores, which can then be organized into a pore-size distribution. This study finds that the shape of the pore-size distribution is influenced by the type of carbonate rock, where the primary difference between scanned samples is their pore structure. Statistical parameters are calculated by fitting a lognormal distribution function to each sample’s pore-size distribution.
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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) | 0.000 | 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 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".