Numerical Well Testing Using Unstructured PEBI Grids
Bibliographic record
Abstract
Abstract History matching is challenging for low permeability gas reservoirs because of significant differences between the static properties in a geostatistical model and in-situ properties measured from well testing. The literature has documented that reduction of in-situ permeability due to overburden pressure can be in two orders of magnitude. Numerical well testing provides a way of tuning a static model with dynamic well testing information. However, a traditional single well testing model using Cartesian LGR (local grid refinement) is not ideal for predicting the pressure transient behavior. Furthermore, a stand-along well testing conditioned model cannot be fully coupled into a full-field model to honor the flow regime. This paper presents a methodology of using a PEBI (perpendicular bisector) grid in a simulation model to match well test data for a low permeability gas reservoir in Canadian Foothills. A PEBI-LGR grid is created around the well and a vertical hydraulic fracture is implemented to match the post fracture pressure build up. History matched parameters include static properties and hydraulic fracture properties such as fracture half length and fracture permeability. Finally, well testing conditioned effective properties will be stochastically populated into the simulation model for the full field history matching. In conclusion, the PEBI gridding technique links well testing with reservoir simulation and provides the most efficient workflow in modeling unconventional gas reservoirs with multi-stage hydraulic 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".