3D Analytical Modeling of Hydraulic Fracturing Stimulated Reservoir Volume
Bibliographic record
Abstract
Abstract An easy to use analytical 3D model is developed to simulate the growth of stimulated reservoir volume (SRV) with time once a hydraulic fracturing job is started in an anisotropic poroelastic medium. In the proposed method, diffusivity coefficients in three dimensions are determined first by calibrating the model with an actual 3D microseismic-event cloud. Then the geometry of SRV is predicted under different stimulation conditions. The method permits studying the geometry of hydraulic-fracturing SRVs in both vertical and horizontal wells. The analytical model is corroborated with the use of a numerical simulator. The proposed method is important because in unconventional low-permeability reservoirs, such as shale and tight gas reservoirs, productivity depends primarily on permeability of the SRV and the reservoir area contacted by the SRV. Microseismic monitoring has been shown to be a useful technology to study the characteristics of hydraulic fractures. As such, the optimum is to constrain the analytical 3D model developed in this study with the use of microseismic data. It is concluded that this easy to use, yet accurate analytical model, is a viable tool for analyzing the orientation and geometry of hydraulic-fracturing SRV and for predicting other SRVs in the same reservoir. Examples of applications which can be reproduced easily in a spread sheet are presented in detail.
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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 | 0.000 | 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) | 0.001 | 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 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".