AN EXPERIMENTAL ANALYSIS IN EVALUATING ORDERING CRITERIA FOR COMPOSITE CORE ARRANGEMENT IN SPECIAL CORE ANALYSIS
Bibliographic record
Abstract
Composite cores of core plugs from different heights of an exploration well core are often used for routine and special core analysis to evaluate an oil reservoir. The question is whether or not the order of the core plugs in the composite core make a difference to the absolute and relative permeability measurements? The seminal work by Huppler (1969), proposed ordering individual core samples harmonically in a composite core in order to match the overall permeability. Langaas (1998) proposed ordering the core samples in decreasing permeability for effective relative permeability measurements, based on theoretical framework of North Sea sandstones. In this paper we tested the methods proposed by Huppler and Langaas experimentally. Core samples with permeability ranging from 50 mD to 2 Darcy were used for this work. The orientation of the core samples was tested for permeability and relative permeability and compared with the theoretical model developed by Langaas. In addition to the experimental work, simulations were performed with the three different composite cores and the representative recovery factor was compared to provide the appropriate composite core arrangement.
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.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".