Study of Cluster Efficiency in Unconventional Reservoirs by Analytical Simulators
Bibliographic record
Abstract
Abstract Unconventional reservoirs have been defined as formations that cannot be produced at economic flow rates or that do not produce economic volumes of oil and gas without horizontal well with hydraulic fracture treatments. Horizontal well fracturing efficiency in unconventional reservoirs is the main factor for the success of developing unconventional reservoirs. The early focus of the industry was on the operational efficiency and during this period, the geometric spacing of perforation clusters adopted as the preferred completion method. Cipolla et al. (2011) presented a case study on the interpretation of production logs from hundreds of horizontal wells. The results indicated that 60% of perforation clusters contribute to production when completed geometrically and completion cost could reach more than 60% of the total well cost. Recently, numerous studies have been undertaken to understand this phenomenon. Increasing the stimulation effectiveness and maximizing the number of perforation clusters contributing to productivity was an obvious area for improvement to engineer the completion design. The uniform initiation and distribution of fractures in each frac stage is very complex because there are many factors affecting the fracture initiation such as stress orientation, heterogeneity, existing of natural fractures, and completion design. This paper presents sensitivity studies investigating the effect of the formation permeability, fracture spacing, fracture half-length, fracture conductivity, flowing bottom hole pressure, and outer reservoir permeability on the well ultimate recovery efficiency by using analytical simulator.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".