Sensitivity Analysis of Unstructured Meshing Parameters on Production Forecast of Hydraulically Fractured Horizontal Wells
Bibliographic record
Abstract
Abstract In the context of multi-stage hydraulically fractured horizontal wells, unstructured grids such as PEBI (perpendicular bisector) grids or Voronoi grids have been widely applied to generate simulation meshes for fracture networks. In previous work we developed a robust optimization-based 2D meshing algorithms to handle non-orthogonal, low-angle intersections of extensively-clustered fractures with non-uniform aperture distributions. However, vertically extruded 2.5D PEBI grids become necessary with more wells being drilled into dipping layers. Besides, it is necessary to provide practical application suggestions for choosing an unstructured mesh to accurately resolve flow regimes of production. In this work we extended 2D PEBI workflow to 2.5D, and then validated it against two models: (a) a synthetic model with one horizontal well and 120 orthogonal intersected hydraulic fractures built by Tar-tan grid and (b) a field-scale model with three horizontal wells and 120 non-orthogonal intersected hydraulic fractures in a slightly dipping reservoir created by a commercial software plug-in. For the synthetic model, we simulated production performance at a constant bottom-hole flowing pressure up to 20 years. For the field-scale model, we first performed history matching and then predicted production at a constant rate up to 20 years. We compared pressure diffusion front, bottom-hole flowing pressure as well as CPU performance. Reasonably good matches between PEBI grids and other grids are observed in both pressure diffusion front and production behavior. Sensitivity analysis suggests that refinement around the fractures has modest impact to early time production while background density has dominant impact to the late time production. Background grid type and grid orientation have less influence as long as they have the same grid density. Less number of 2.5D PEBI cells can be achieved by removing unnecessary refinement around fractures, increasing reservoir background size and reservoir background size ratio, replacing unstructured background grids with structured grids, and reducing the complexity of the fracture networks without loss of the accuracy and therefore result in much favorable CPU performance. The existence of opened natural fractures was evaluated to simulate the out-of-zone production. The results suggest that effective drainage area can be significantly greater with the synergy of the natural fractures. This study is the first to apply unstructured grids to simulate multiple horizontal wells with irregular hydraulic fractures. Besides, this paper provides detailed discussions of implementation algorithms as well as comparisons between 2.5D PEBI and LGR based grids in the context of fracture modeling. And most importantly, this study answers the question regarding how to choose an appropriate 2.5D PEBI mesh to yield both accurate results and good CPU performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".