Reconstruction of 3D Network Model Through CT Scanning
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it