Extreme Limited-Entry Design Improves Distribution Efficiency in Plug-and-Perforate Completions: Insights From Fiber-Optic Diagnostics
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Bibliographic record
Abstract
Summary Limited-entry (LE) plug-and-perforate (PnP) fracture designs were pioneered in the early 1960s as a cost-effective technique to stimulate multiple pay zones with varying stress regimes (Murphy and Juch 1960). Conventional completion techniques involved blanket perforating the entire interval at a certain number of shots per foot (spf). The LE technique was revolutionary in that it recommended “limiting” the number of perforations to distribute fracture-stimulation fluids into multiple intervals with differing stress regimes. However, diagnostics have shown that LE-treatment distribution during the slurry phase is uneven, and is highly affected by several key parameters that may change significantly during treatment. Several papers have been published on the inefficiencies associated with LE design and what can be performed to overcome them (Ugueto et al. 2016; Somanchi et al. 2016). Shell Canada Limited recently tested extreme limited-entry (XLE) designs to determine if additional pressure drop across the perforations would improve treatment distribution. Stages were alternated with differential perforation friction (ΔP) pressures of 2,000, 2,500, and 3,000 psi to determine if there was a threshold ΔP that would result in a more-optimal treatment distribution. However, because of wellhead-pressure limitations, actual ΔPs were below the design values. There were no placement issues associated with fewer perforations and higher treatment pressures. The trial well was completed with thirteen three-cluster stages. All clusters were spaced evenly at 50 m and fracture-stimulated with a slickwater system with 31 tons/cluster (93 tons/stage). The fracture stimulation was monitored with an externally clamped fiber-optic (FO) cable. Treatment distribution and production were quantified by using distributed acoustic sensing (DAS) (Molenaar and Cox 2013). Post-job analysis indicates a 40% improvement in distribution compared with previously stimulated three-cluster standard LE completions. With the XLE design, 100% of the clusters received some proppant. There is a 33% increase in cluster activity at IP90 (initial production on the 90th day) from the XLE design compared with a previously completed three-cluster conventional LE well. Improvement in distribution is minimal beyond ΔP of 1,200 psi during the pad phase. However, this threshold could be rock-specific and needs to be validated with trials in different play types. Data also suggest that treatment pressure should be maintained at a maximum throughout the pad and slurry placements, within equipment and wellhead limitations. During the pad, this is important to ensure breakdown and fracture extension. In the slurry phase, maximizing out pressure helps to maintain ΔP across eroding perforations. In some plays, insufficient ΔP may prevent all clusters from breaking down. In Groundbirch, typically all clusters break down and take fluid from the start but screen out as soon as sand hits. Typically, slurry rate was not increased to compensate for the loss in ΔP associated with an increase in perforation diameter. These factors are mainly responsible for the heel-vs.-toe bias in LE designs, which results in undertreatment of toe clusters (Ugueto et al. 2016).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it