Understanding Compressibility Coefficients in Heterogeneous Fractured Rocks and Implications for CSG Reservoir Simulation
Bibliographic record
Abstract
Abstract A variety of different compressibility terms, including cleat compressibility and bulk compressibility, are used when modelling coal seam gas (CSG) reservoirs. The relationship between different compressibility terms is often theoretically straightforward, but in practice may be much more complex, particularly when considering heterogeneous and/or fractured rocks. This paper outlines experimental work measuring different compressibility terms using printed rock samples, and analysis that demonstrates some of the challenges associated with relating these compressibilities. Three-dimensional printed rock samples with heterogeneity (layers of different stiffness), some of which included planar fractures, were created. The compressibility of these samples was measured based on changes in permeability (as might be used to estimate cleat/fracture compressibility) and also based on volumetric strain. Simple models were history matched to estimate the cleat compressibility, which is then used to calculate a bulk compressibility based on theoretical relationships. This is then compared to the bulk compressibility measurement based on volumetric strain. Initial results indicate that the relationship between the different compressibility terms is much more complex than theory suggests. The theoretical relationship of bulk compressibility with pore compressibility yields values up to one order of magnitude different from that of laboratory measurements. Our study highlights the importance of cleat compressibility in modelling CSG reservoirs and the significance of bulk compressibility in estimating deformation associated with CSG production. We believe our findings will contribute to a better understanding of compressibility terms in CSG reservoir modelling and encourage further research in this area.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".