Coupled 4D Flow-Geomechanics Simulation to Characterize Dynamic Fracture Propagation in Tight Sandstone Reservoirs
Bibliographic record
Abstract
Although China's low-permeability and tight oil reservoir utilization and newly proven reserves are growing annually, the overall recovery of such reservoirs is generally low. One of the main factors influencing the low recovery is the effect of intricate dynamic fracture propagations on the remaining oil distribution. Constrained by the evolution of an in situ stress field and the accumulation of fluid injection volumes, the growth of dynamic fractures allows a production profile of water breakthrough. To reveal this phenomenon, this paper systematically summarized the workflow of geoengineering integration and took the target block of an oil reservoir in the Changqing oilfield as an example. The numerical simulation model and geomechanical model of the reservoir were established, respectively, by using core analysis and rock mechanics experiments, and the simulation of dynamic fracture propagation under the control of flow-geomechanical coupling mechanisms was carried out. It is found that injection-induced fractures open and propagate under propulsion pressure until they link with natural fractures when the injection pressure surpasses the formation rupture pressure. The natural fractures become effective after long-term water injection and propagate until they connect to hydraulic fractures near a producing well. The study results on the dynamic fracture propagation process have theoretical and practical implications for the characterization of fractures, residual oil production, and water control techniques in tight sandstone reservoirs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".