A novel pulse decay method for evaluating shale bedding laminae permeability: Distinguishing matrix and bedding contributions
Bibliographic record
Abstract
As the global demand for clean energy increases, shale gas has emerged as a vital component of natural gas resources and a focal point for research and development. Precise evaluation of the permeability of low-permeability shale rocks is essential for optimizing extraction strategies, such as horizontal drilling and multi-stage fracturing. However, current permeability testing methods face significant challenges, particularly in differentiating between the permeability characteristics of the shale matrix and bedding laminae, which is crucial for understanding gas flow behavior in shale formations. In this paper, we introduce a novel pulse decay method for assessing the permeability of shale bedding laminae, with a focus on the early-stage pressure transmission process. The method develops a nonlinear governing equation that describes the gas flow behavior through bedding laminae and provides an analytical solution for bedding laminae permeability based on early-stage pressure transients. Experimental validation using shale samples from the Longmaxi Formation in the Sichuan Basin, China (with helium as the test gas), shows that the calculated apparent permeability of the samples is in good agreement with the experimental results. Further numerical simulations confirmed the validity of the method. The results indicate that the calculated permeability of the bedding laminae closely matches the model input values and demonstrates improved accuracy compared to conventional pulse decay methods. This new method provides a more accurate means of measuring the permeability of bedding laminae in shale, particularly for shale with well-developed bedding structures. As a valuable complement to traditional pulse decay methods, this approach enhances our ability to characterize the permeability properties of low-permeability rocks by focusing on the early flow stage, thereby contributing to more efficient shale gas resource development.
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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 | 0.001 | 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 | 0.000 | 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".