Varying least principal stress along lithofacies in gas shale reservoirs: effects of frictional strength and viscoelastic stress relaxation
Bibliographic record
Abstract
Compositional variation of rock properties in unconventional shale reservoirs impacts not only hydraulic fracturing stimulation but also the variability of induced shear slip on pre-existing fracture networks. Lithological layering is also responsible for varying stress states in various unconventional reservoirs subjected to normal, strike-slip, reverse or hybrid faulting regime (a combination of any two of them). Three possible mechanisms are considered to evaluate the layer-based stress profile in the sub-surface, that is viscoelastic stress relaxation, elevated pore pressure and variation in rock frictional strength. We conducted multistage triaxial deformation tests with varying strain rates (5 × 10−7 to 10−5 s−1) on shale sample originating from three lithological units of the Goldwyer shale formation, that is G-I, G-II and G-III in the Broome platform of onshore Canning basin. Additionally, pore pressure was estimated from wireline logs, and tectonic stress accumulation was modelled using time-dependent deformation (creep) data. The above analysis suggests that varying frictional strength alone could not explain the variation of the least principal stress; however, the inclusion of stress relaxation also contributes, whereas elevated pore pressure has no effect. Using this approach, we could also relate the changes in minimum principal stress (Shmin) from one lithofacies to the other and anticipate how this will impact the optimum design of landing zone during horizontal drilling and completion in unconventional field development.
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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 | 0.000 |
| 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 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".