Quantifying Reservoir Permeability in Shale Reservoirs Using After-Closure Analysis of DFIT by Considering Natural Fractures, Fissures, and Microfractures: Field Application
Bibliographic record
Abstract
Abstract Natural fractures, fissures, and microfractures are well-known contributors to production performance of shale reservoirs. Complex fracture geometries can be generated by either small-scale fracturing, DFIT, or large volume fracture stimulation, because the activation of pre-existing natural fractures, fissures, and microfractures plays a significant role on generation of induced hydraulic fractures. Therefore, much more attention should be paid to the model development of DFIT complexity. In previous work, Chen et al. (2017a) built a complex fracture-network model for after-closure analysis of DIFT by considering natural fractures. Based on that, this paper introduces a generalized model with random fracture geometry caused by natural fractures, fissures, and microfractures. First, the model flexibility is demonstrated by different complex fracture cases, namely opening-fissure fracture network, tree-like fracture network, radial multiple fracture network, and mutually orthogonal fracture network. It is found that the pressure derivative reaches constant level, no matter what the fracture geometry is. Furthermore, the reservoir permeability of field examples from actual DFIT tests in Marcellus shale reservoir is quantified using the log-log diagnostic plots based on the model solutions. Finally, the Nolte G-function is applied to verify the estimated results. We find that the results from the two methods are consistent. This work primarily focuses on quantifying the reservoir permeability, while in the future more efforts will be made to identify the fracture properties using the proposed model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".