New Models of Brittleness Index for Shale Gas Reservoirs: Weights of Brittle Minerals and Rock Mechanics Parameters
Bibliographic record
Abstract
Abstract Brittleness indices (BI) commonly used in the petroleum industry are based on elastic modulus or mineralogy that can be calculated from well logs. However, they ignore the weights of these two factors. Also, it is imprecise to calculate BI by considering quartz (or dolomite) as the only brittle mineral in mineralogy-based BI prediction. Shale gas reservoirs like Eagle Ford are rich in carbonate minerals. If the carbonate minerals are ignored in those reservoirs, the value of BI will be greatly underestimated. On the other hand, brittle minerals like quartz, dolomite and calcite play different roles in BI calculation. If we equally treat them without weighting in BI prediction, the BI being calculated will be inaccurate as well. This paper analyzes the influence of calcite on rock mechanics parameters and BI comparing with quartz and clay. Then new models of BI prediction are built to characterize the weight of each brittle mineral and rock mechanics parameter. Based on the least squares method, optimal values of weight coefficients will be obtained by iteration. The results show that calcite improves rock brittleness and should be considered as a brittle mineral in BI prediction. However, the weight of calcite is less than quartz. From the statistics results, quartz > dolomite > calcite > clay occurs in improving BI. The results also show that Young's modulus plays a more important role in BI prediction than Poisson's ratio.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".