Grid-Sensitivity Analysis and Comparison Between Unstructured Perpendicular Bisector and Structured Tartan/Local-Grid-Refinement Grids for Hydraulically Fractured Horizontal Wells in Eagle Ford Formation With Complicated Natural Fractures
Bibliographic record
Abstract
Summary In the context of modeling fractured horizontal wells, unstructured grids have been applied to generate simulation meshes for complex fracture networks. It is necessary to investigate how to choose an unstructured mesh to accurately simulate production performance. In this paper, a new unstructured gridding and discretization work flow is proposed to handle nonorthogonal and low-angle intersections of extensively clustered fractures with nonuniform apertures. The work flow is then validated with two models in terms of production behavior and central-processing-unit (CPU) performance: a synthetic model with one horizontal well and orthogonal intersected hydraulic fractures built by tartan grid, and a field-scale local-grid-refinement (LGR) model with three horizontal wells and irregular hydraulic fractures in a slightly dipping reservoir created by a commercial software plug-in. Good-quality matches are obtained between unstructured and structured grids in both pressure and production behavior. Sensitivity analysis of the meshing parameters suggests that refinement in the vicinity of fractures has improved both early and late production of a well, whereas background density has a dominant effect on the late production. Background-grid type and orientation have less influence as long as they have the same grid density. Fewer cells can be achieved by increasing reservoir-background size and size-progression ratio, replacing unstructured-background grids with structured grids, and reducing the complexity of the fracture networks without loss of the accuracy, resulting in improved CPU performance. This study applies unstructured grids to simulate multiple horizontal wells with complicated fracture networks, and provides detailed comparisons between unstructured and structured grids. Most importantly, it resolves the question regarding how to choose an appropriate mesh to yield both accurate results and high-quality CPU performance.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".