Engineered Fracture Spacing Staging and Perforation Cluster Spacing Optimization for Multistage Fracturing Horizontal Wells
Bibliographic record
Abstract
Abstract Horizontal drilling and multistage hydraulic fracturing applied in tight reservoirs in North America over the past decade and economic productivity attained by creating large fracture surface area to contact the reservoir and create the conductive pathway for the flow of hydrocarbon into the wellbore. Perforation cluster spacing and fracture stagging are keys to successful hydraulic fracturing treatment for horizontal wells. The early focus of the industry was on the operational efficiency. A geometric spacing of perforation clusters adopted as the preferred completion method. Cipolla (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. 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 engineering the completion design. An area with a limited number of horizontally multistage fractured appraisal wells in the Western Desert of Egypt is targeted for this study. The developed workflow comprises integration of petrophysical, geomechanical and production data analysis to evaluate reservoir and completion qualities and quantify the heterogeneity and selectively place perforation clusters in "like-rock" thus promoting the chance of initiating all perforation clusters within a stage and ensuring uniform placement and distribution of the induced fractures and that every cluster in each zone is fracture stimulated and can contribute to the well's full potential. The hydraulic fracture attributes from the fracture simulator were exported to the reservoir simulator. The surface production measurement together with the production profile was used to calibrate the reservoir model. The scope of this study is to present an integrated workflow to identify reservoir properties variation along the lateral section of horizontal to engineer completion design, improve stimulation effectiveness, and improve cluster efficiency. The methodology adopted in this study resulted in optimized fracture design that helped reduce-cost and increased well EUR. Optimal cluster spacing was determined based on long-term production performance. The final calibrated hydraulic fracture and reservoir models were used to optimize the cluster spacing and other completion parameters.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".