An Integrated Approach to Optimize Perforation Cluster Parameters for Horizontal Wells in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract Perforation parameters have a great influence on the performance of the multi-stage fracturing horizontal wells in tight oil reservoirs. Optimizing perforation cluster parameters is able to solve many detrimental issues, including many null perforation clusters without the produced oil, the unevenly distributed output in each stage along horizontal wells, and no complex fracture network always created near the fractured wellbore. To achieve the better performance of the volume fracturing, a practical integrated approach is proposed to optimize perforation cluster parameters. First, based on the good logging data, we establish an evaluation method for the fracability and reservoir properties to select the perforation interval. Second, a mathematical model based on the stress shadow and hydraulic fracture propagation are proposed to optimize the cluster spacing and cluster parameters within each cluster in the same stage, and the un-uniform cluster spacing and perforation number in each cluster are studied. Finally, a case well is successfully conducted with the proposed approach in the tight oil reservoir. Results show that i) the lateral with higher fracability index and property index can be treated as perforation intervals; ii) the un-uniform perforation cluster spacing and the uneven perforation number can obtain a more uniform fracture propagation morphology. The approach can better prevent the generation of ineffective perforation clusters and obtain more complex fracture networks and a better SRV. This also guides to design completion strategy and improves the economic benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".