Accurate stress measurement using hydraulic fracturing in deep low-permeability reservoirs: Challenges and research directions
Bibliographic record
Abstract
Although there is increasing recognition of the significance of deep in-situ stress measurement for the safe and efficient exploitation of geo-energy in deep low-permeability reservoirs, accurate measurement of deep stresses using the hydraulic fracturing technique still requires substantial enhancement. In this work, the major challenges in the precise hydraulic fracturing stress measurement in deep low-permeability reservoirs are pointed out, including high rock temperature, high pore pressure, fracturing mechanism, rock tensile strength, and drilling conditions. Under such circumstances, several future research directions are proposed accordingly. These involve the thermal-pore-elastic effect, downhole sensors and flow meters, appropriate indoor tensile strength test methods, new stress calculation methods, hybrid test techniques, and refined coupled numerical models. The future research recommendations will provide several fresh perspectives for geo-energy development in deep low-permeability reservoirs in subsequent stages. Document Type: Perspective Cited as: Li, P., Liu, Y., Cai, M., Miao, S., Dai, L., Gorjian, M. Accurate stress measurement using hydraulic fracturing in deep low-permeability reservoirs: Challenges and research directions. Advances in Geo-Energy Research, 2024, 14(3): 165-169. https://doi.org/10.46690/ager.2024.12.02
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".