Stress rotations and compounding pore-pressures from multiple well injections
Bibliographic record
Abstract
Multi-stage, multi-well completions cause pore-pressures to increase around each stage treated, compound from earlier offset treatment stages, then dissipate as the injected fluid leaks off into the rock formation. Local stress changes illuminated by microseismic focal mechanisms can be used to create maps of high and low pore-pressures which, in turn, can be used to guide a dynamic slurry propagation model and estimate fluid and proppant distribution from the injection. Injected slurry volumes respond to these pore-pressure changes dependent upon lag time from previously treated stages. An example is presented from a multi-stage, multi-well hydraulic stimulation in the Wolfcamp Formation located in southeast New Mexico. In this location, previous researchers have identified that a normal-faulting stress regime exists with maximum horizontal stress (SHmax) oriented between N75°E and N83°E with intermediate horizontal stress anisotropy. Results from this study shows that SHmax=N80°E and stress anisotropy, 𝜙=0.36in the virgin stress state. During hydraulic stimulation horizontal stress anisotropy is reduced (𝜙=0.33) due to stress shadowing and SHmaxrotates ~+/-24°. Increased pore-pressures from previous treatments remainelevated for ~7 days confining fluid distribution to near the well on ensuing stages. Sufficient pressure dissipates after leakoff providing opportunity for the fluid to propagate into previously opened fractures. Pore-pressure highs can be identified using microseismic hypocenters fitting an altered stress state which differs from events fitting the background unpressured virgin stress state. Since injected fluid migrates toward low pressures and away from highs, we suggest that virgin stress events can be used to guide injected slurry volumes including proppant.
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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 | 0.000 | 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) | 0.002 | 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".