Permeability inversion using induced microseismicity: A case study for the Longmaxi shale gas reservoir
Bibliographic record
Abstract
Abstract We have predicted the flow permeability and its spatial distribution for the Longmaxi shale gas reservoir using microseismicity induced during hydraulic-fracturing stimulation. In the time-of-occurrence versus distance-from-injector plot, we find that microseismic points exhibit a parabolic envelope, which we interpret as a triggering front. This reveals that fluid pressure diffusion is at least one of the underlying mechanisms of microseismicity generation. We derive the large-scale equivalent diffusivity from the triggering front plot and thereafter obtain a 3D diffusivity map of the heterogeneous reservoir by solving an eikonal-like equation suggested previously. During this process, we apply kriging interpolation to increase the density of sparsely distributed microseismic points. The resulting diffusivity ranges between 1.0 and 25.85 m2 s−1 with the peak probability attained at 1.8 m2 s−1, which is consistent with the estimate we obtain from the triggering front analysis. We transform the diffusivity map into a permeability map using three different theories of fluid pressure diffusion in porous media: the seismicity-based reservoir characterization method (SBRC) based on Biot’s theory of poroelasticity, the quasirigid medium approximation (QRMA), and the deformable medium approximation (DMA) based on the de la Cruz-Spanos theory. The permeability according to QRMA is slightly higher than that from SBRC, yet we observe no significant difference. However, these estimates are by one order of magnitude higher compared with the permeability estimate from DMA. Furthermore, the permeability from all three theories is much higher than that from previously reported core sample measurements. We interpret this as the difference between large-scale equivalent and matrix permeability and therefore lend weight to the hypothesis that there exist highly conducting fluid pathways, such as natural fractures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".