Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 2: Selected Stage Forecasting
Bibliographic record
Abstract
The current practice for multi-fractured horizontal well development in low-permeability reservoirs is to complete the full length of the well with evenly spaced fracture stages. Given methods to evaluate along-well variability in reservoir quality and to predict stage-by-stage performance, it may be possible to reduce the number of stages completed in a well without a significant sacrifice in well performance. Provision and demonstration of these methods is the goal of the current two-part study. In Part 1 of this study, reservoir and completion quality were evaluated along the length of a horizontal well in the Montney Formation in western Canada. In the current (Part 2) study, the along-well reservoir property estimates are first used to forecast per-stage production variability, and then used to evaluate production performance of the well when fewer stages are completed in higher quality reservoir. A rigorous and fast semi-analytical model was used for forecasting, with constraints on fracture geometry obtained from numerical model history matching of the studied Montney well flowback data. It is concluded that a significant reduction in the number of stages from 50 (what was implemented) to less than 40 could have yielded most of the oil production obtained over the forecast period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".