Efficient fully coupled 3D poroelastic modeling of geomechanical deformation during depletion and reinjection: An asymptotic transformation of Biot’s poroelasticity from a dynamic to a quasistatic response
Bibliographic record
Abstract
ABSTRACT We develop an approach for efficient 3D simulation of the quasistatic fully coupled poroelastic response of a reservoir during depletion and subsequent reinjection. The approach uses a scaling of the solid and fluid densities in Biot’s poroelastic equations. This scaling impacts the critical frequency fc of Biot’s slow wave that defines diffusive flow (f fc). We find the criterion for the density scaling range over which the poroelastic response is accurately modeled and benchmark the approach against Terzaghi’s 1D and Rudnicki’s 3D analytic solutions. The density scaling approach is presently limited to single-phase fluid flow. To illustrate the utility of this approach, we simulate microseismic depletion delineation (MDD) in a fractured unconventional reservoir. The reservoir, which is subjected to an anisotropic stress field, is first produced for 1000 days, and then a reinjection (below the in situ pressure) is performed for 100 days. We find that stress reorientation during production produces favorable conditions for the generation of Mohr-Coulomb slip-related microseismicity. The locations of these microseismic events are found to be consistent with depleted portions of the fracture system, in accordance with the MDD concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".