Importance of Fabric on the Production of Gas Shales
Bibliographic record
Abstract
Abstract The heterogeneity and complexity of gas shales cause substantial and often inexplicable variability in the production histories of gas wells. A major factor contributing to this variability is the microfabric of the matrix and the fracture network of the reservoir. It is widely postulated, although not proven, that the gas production from shales is controlled principally by Darcy flow through the fracture system and the matrix is considered important principally for gas storage. In order to gain insight and test the relative importance of fracture spacing and matrix diffusion/flow on the production of gas shales, we have developed a 2-dimensional numerical simulation model, which considers the flow of gas through both the shale matrices and the fractures for varying fabrics utilising experimental data obtained from a variety of important gas shales. The results of initial, constant parameter, numerical simulations showed that for a wide range of relative fracture permeability, matrix permeability/diffusivity and fracture spacing, the productivity of a gas shale reservoir is dependent on matrix diffusion rates. The diffusion rates and stress dependent fracture permeability data when integrated into the numerical simulator can be tested against measured production histories leading to more accurate production forecasts of new reservoirs and optimisation of field design.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".