A review of plug-and-perforate, ball-and-seat, and single-entry pinpoint fracturing performance in the unconventional montney reservoir
Bibliographic record
Abstract
Abstract The Montney tight siltstone reservoir is a multi-stacked play, which covers a total area of 130,000 km 2 along the Alberta–British Columbia border in Western Canada. Since the late 1990s, different fracturing methods and fluids have been tested in 4000-plus Montney wells to find the optimal fracturing method and fluid. The previous studies reviewing the performance of fracturing methods in Montney do not represent a holistic evaluation of these methods, owing to some limitations, including: (1) using a small sample size, (2) having a limited scope by focusing on a specific aspect of fracturing (method/fluid), (3) relying on data analytics approaches that offer limited subsurface insight, and (4) generating misleading results (e.g., on optimum fracturing method/fluid) through using disparate data that are unstructured and untrustworthy as a result of significant regional variations in true vertical depth (TVD), geological properties, fluid windows, completed lateral length, fracturing method/fluid/date, and drawdown rate management and choke size strategy. The present study eliminates these limitations by rigorously clustering 4000-plus Montney wells based on the TVD, fluid window, completed lateral length, fracturing method/fluid/date, and drawdown rate strategy to isolate the effect of each fracturing method by comparing each well’s production to that of its offsets, which use different fracturing methods but possess similar geology, fluid window, and TVD. With similar TVD, fracturing fluid, and completion date, wells completed with pinpoint fracturing method outperform their offsets completed with ball-and-seat and plug-and-perforate (PnP) fracturing methods. However, wells completed with ball-and-seat and PnP methods that outperform their offset pinpoint wells have either: (1) been fractured one to four years earlier than the pinpoint wells and/or (2) used energized oil-based fluid, hybrid fluid, and energized slickwater versus slickwater used in the pinpoint offsets. This suggests that the water-phase trapping is more severe in these pinpoint wells attributable to the use of slickwater. Previous studies often favored one specific fracturing method or fluid without highlighting these complex interplays between the type of fracturing method, fracturing fluid, completion date (regional depletion or well vintage), and the reservoir properties and hydrodynamics.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".