Determining the Most Effective Diversion Strategy Using Pressure Based Fracture Maps : A Meramec STACK Case Study
Bibliographic record
Abstract
Abstract This paper discusses a STACK (Sooner Trend Anadarko Basin Canadian and Kingfisher Counties) case study that determined the effectiveness of different diversion techniques, including pods, sand ramps with sand slugs, rate cycling, and utilization of the completions order to control fracture growth. A secondary goal of this study was to evaluate the suitability of pressure-based fracture maps and oil and water phase tracers in monitoring diverter effectiveness. Effectiveness of a given diverter technique and diverter drop was evaluated using the two techniques on a 3-well pad. The three wells were completed using a combination of: 4 pods per treatment interval6 pods per treatment interval8 pods per treatment intervalhigh-volume proppant loading per treatment interval The effectiveness of the diverter drop was evaluated using each of the diagnostic techniques listed above. The pressure-based fracture analysis uses the pressure response recorded in an isolated stage in the monitor well to compute fracture geometry and the rate of growth of the fracture dimensions. The effectiveness of a given diverter drop is classified into one of four possible categories: stop dominant fracture growth, impede dominant fracture growth, no impact on growth of dominant fracture and accelerate the growth of dominant farcture. These results were then compared with the analysis from oil and water phase tracers and treatment pressure analysis. Successful (effective) diversion was observed on 82 % of the stages with pods compared to 64% successful diversion where sand ramps were used. In addition, stages using 8 pods for diversion had a 15% reduction in average fracture half-length compared to stages using 4 pods. Fracture height was better controlled through the order of completions of the stages between 3 wells. Completing the middle well in the upper part of the zone ahead of the two outer wells in the lower part of the zone, controlled the vertical height growth of the two outer wells. The offset pressure-based analysis proved to be as effective in accurately diagnosing the diverter effectiveness and provided a significant cost and timing advantage compared to other diagnostic techniques.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".