Reconstruction of 3D Network Model Through CT Scanning
Bibliographic record
Abstract
Abstract Digitized description of rock is one trend of flow modeling. The paper presents a method for reconstructing 3D network model of microscopic pore structure according to CT images of rock. Sequential CT images which can fully describe 2D microscopic pore structure of rock are obtained by using ACTIS-225FFi CT/DR/RTR microfocus CT equipment. 3D skeleton and pore-bodies of the porous media can be obtained through processing these images by using the thinning algorithm. On the base of analyzing the differences between the core model and the network model, pores and throats are extracted considering the geometrical equivalence through equivalent method of flow conductivity and shape factor. Thus, the conversion from CT scanned images of real rock into a 3D network model is realized, which can be used as a powerful tool in flow simulation. One advantage of the method lies in the fact that simplified geometrical objects, such as pores and throats, can be used to replace the irregular geometry with less calculating time while retaining the geometrical features and flow characters. Based on the method above mentioned, the paper takes well 70-1 of Kendong oilfield, China as an example to carry out the CT scanning experiment and to reconstruct the network model. It is found that there is a good agreement between the calculated parameters of network model and those of porosity, absolute permeability, capillary pressure curves as well as relative permeability curve measured in laboratory, which indicates that the network model can fully describe the microscopic pore and throat sizes as well as topology of rock.
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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) | 0.001 | 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 | 0.000 | 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".