Extreme Limited Entry Design Improves Distribution Efficiency in Plug-n-Perf Completions: Insights from Fiber-Optic Diagnostics
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Bibliographic record
Abstract
Abstract Limited entry (LE) plug ‘n’ perf (PnP) fracture designs were pioneered in the early 1960s as a cost-effective technique to stimulate multiple pay zones with varying stress regimes (Murphy & Juch, 1960). Conventional completion techniques would involve blanket perforating the entire interval with 4 shots per foot. The 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 impacted 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 done to overcome them (Ugueto, Huckabee, Molenaar, Wyker, & Somanchi, 2016) (Somanchi, O' Brien, Huckabee, & Ugueto, 2016). Shell Canada Ltd. 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 (∆P) pressures of 2,000, 2,500, and 3,000 psi to determine if there was a threshhold ∆P that would result in a more optimal treatment distribution. However, due to wellhead pressure limitations, actual ∆Ps 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 3-cluster stages. All clusters were evenly spaced at 50 meters and fracture stimulated with a slickwater system with 31 tons/cluster (93T/stage). The fracture stimulation was monitored using an externally clamped fiber-optic (FO) cable. Treatment distribution and production were quantified using Distributed Acoustic Sensing (DAS) (Molenaar & Cox, 2013). Post-job analysis indicates a 40% improvement in distribution compared to previously stimulated 3-cluster standard LE completions. With the XLE design, 100% of clusters received some proppant. There is a 33% increase in cluster activity at IP90 from the XLE design compared to a previously completed 3-cluster conventional LE well. Improvement in distribution is minimal beyond ∆P of 1200 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 suggests that treatment pressure should be maintained at a maximum throughout the pad and slurry placement, within equipment and wellhead limitations. During pad, this is important to ensure breakdown and fracture extension. In the slurry phase, maxing out pressure helps to maintain ∆P across eroding perforations. In some plays, insufficient ∆P may prevent all clusters from breaking down. In Groundbirch, typically all clusters breakdown and take fluid from the start but screenout as soon as sand hits. Howeever, slurry rate is typically not increased to compensate for the loss in ∆P due to an increase in perf diameter. These factors are largely responsible for the heel-toe bias in LE designs which results in under-treatment of toe clusers. (Ugueto, Huckabee, Molenaar, Wyker, & Somanchi, 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 | 0.001 |
| 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