Use of Micro-CT Images to Reconstruct Porous Media for Pore Network Model
Bibliographic record
Abstract
The present study is an investigation on the multi-phase flow, specifically oil-water phase flow inside an oil-reservoir using pore network modeling. Pore network model can be effectively used in understanding the transport process of the multiphase flow within the pores of oil reservoirs, which are typically in the range of 2–5 μm. Pore network model consists of two main components: the description of the pore geometry inside a porous rock material and the simulation of micro-scale processes to calculate various fluid flow properties. In the present study, the realistic description of the pore space is obtained using a Berea Sandstone Core sample. A small core of suitable dimension of this core sample is extracted and micro CT images of this sample are taken at a resolution of 2.1 m. Series of images are obtained in the form of cross-sectional view of individual layers as well as its two-dimensional reconstructions. These images are processed to reveal the exact positions of the void and solid spaces inside the rock-structure according to the pixel-distribution. Maximal ball algorithm is chosen and its extended form is applied to the image data to give the three dimensional reconstruction of the rock sample. In the 3D reconstruction, pores and throats are defined separately in a deterministic way. Thus, realistic complete network is possible to extract from high-resolution micro CT images, instead working with an equi-spaced pore throat system, normally used for such modeling. Pore network model calculations of the physical properties are easier to apply on the well-defined network and the property values such as permeability or capillary pressure are matched well against the experimental data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".