A Statistical Method for Lithic Content Based on Core Measurement, Image Analysis and Microscopic Statistics in Sand-conglomerate Reservoir
Bibliographic record
Abstract
The type and content of lithics are of great importance to reservoir research.The existing statistical methods for lithic content mainly focus on sand-level lithics, failing to consider gravel-level lithics.Thus, these methods are not applicable to sand-conglomerate reservoirs, where sand-level and gravel-level lithics coexist.To solve the defects, this paper proposes a hybrid strategy for lithic content in sand-conglomerate reservoirs.Firstly, the type and content of gravel-level lithics were determined through full-bore formation microimager (FMI) imaging logging and core analysis.Next, the type and content of sand-level lithics were obtained by thin-section observation.On this basis, the data on the two levels of lithics were integrated to yield the total content of each type of lithics in the sand-conglomerate reservoir.The hybrid strategy was applied to analyze the lithics features and physical properties of a sand-conglomerate reservoir in the upper part of the fourth member of the Shahejie Formation (upper Es4), north zone, Dongying Depression, China.The results show that the hybrid strategy output greater upper and lower limits of the total lithic contents than the existing methods, and outshined the traditional thin-section observation in accuracy.In the study area, the sand-conglomerate reservoirs in the upper Es4 of the two blocks have different physical properties.The difference is mainly attributable to the different types and contents of lithics of the two blocks.The proposed strategy boasts a strong application potential in oil and gas exploration.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".