Application of Interventionless Single Point Entry Technology to Improve Proppant Placement Control and Well Production
Bibliographic record
Abstract
Abstract The hydraulic fracture treatment (HFT) and its effectiveness to enhance wellbore drainage directly correlate with each well’s overall production performance and underlining economics. This paper will discuss the potential of ultra high stage count Single Point Entry (SPE) sleeves and their ability to increase control over proppant placement and isolation during the HFT as a method for optimizing well performance, economics, and reduce non-uniformity between treatments and wells. To address the limitations of current completion methods, full ID single point entry systems have been developed for open hole and cemented applications. These systems provide unlimited frac stage count with lower frac tortuosity, provide increased control over proppant placement and well production, reduce or eliminate over- flush and formation damage, and achieve higher efficiency during and after frac stimulation than previous conventional plug-and-perf (PnP) and sleeve systems, thereby reducing costs. While the ball-and-seat completion technique revolutionized the efficiency of multi-stage single point entry fracturing, its vast array of limitations (primarily ID restrictions), limited stage count, and compatibility with cemented liners quickly sidelined it in place of PnP. PnP offers increased surface area contact through additional entry points compared to sleeve systems of the past and remains the accepted method for achieving zonal isolation and initiation during stimulation. However, the time intensive operations of PnP present challenges in maintaining efficiencies due to variability in wireline during deployment and coiled tubing during millouts. The increase in number of clusters per stage and number of stages per well achieved with PnP often results in higher stimulated rock volumes (SRV) however, due to the number of multiple clusters open simultaneously, this method gained a "pump-n-pray" reputation due to the uncertainty of cluster efficiency and its unpredictability. The lack of cluster control over the years has created a series of challenges in terms of parent-child well relationships and spacing, economical asset development, and loss of potential production. With over 4,000 stages fracture stimulated across US, Canada, and Asia, some wells containing 220 individual stages, this paper will address the differences in production in terms of bbl of oil equivalent (BOE) for direct and indirect offsets in trials, compare capital efficiency with spud to put on production (POP) timelines, demonstrate economical completion optimization for lower commodity pricing of oil, and carbon intensity reduction measures to lower greenhouse gas emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".