A 3D Finite Element Model for History Matching Hydraulic Fracturing in Unconsolidated Sands Formation
Bibliographic record
Abstract
Abstract Field hydraulic fracturing tests data provide valuable information regarding the mechanical and hydraulic behaviours of the reservoir formation. By history matching the field bottomhole pressure vs. time curve from hydraulic fracturing tests, a set of field-calibrated geomechanical models can be obtained, which is an important asset for any further works on utilizing geomechanics to enhance the injection and production performance. This paper presents a 3D finite element model for history matching the complete bottomhole pressure vs. time curve generated during hydraulic fracturing tests, considering the injection rate as input. To simulate the hydraulic fracturing process in unconsolidated sands formation, a poro-elasto-plastic constitutive model, together with a strain-induced anisotropic full permeability model, are formulated and implemented into a 3D finite element geomechanical simulator. Unlike the conventional simulation of hydraulic fracturing in hard rock, hydraulic fracturing in an unconsolidated sands reservoir is simulated as a large area of shear dilated plastic zone, inside of which the effective stresses are low and hydraulic conductivities are high. It is shown that the proposed numerical model can capture the hydraulic fracture initiation and propagation in unconsolidated sands formation and matches the field pressure vs. time curve.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.002 | 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".