Assessment of Limited Entry Cluster Distribution Effectiveness and Impactful Variables using Perforation Erosion Measurements
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Limited entry perforating is the most common method of diversion used in unconventional hydraulic fracturing. Still it is not immediately apparent how even the stimulation treatments are distributed between the clusters, and the impact of different perforating variables. This paper presents the finding of two well perforation erosion measurement in the Duvernay formation of Western Canada using two logging techniques, optical imaging and ultrasonic measurements. In two wells multiple perforating variables were tested including shot diameter, shot orientation, perforation pressure drop, variable vs constant cluster shot count, and cluster count per stage. Unstimulated perforations were measured for each perforating charge type and orientation to validate baseline perforation dimensions prior to erosion and use to calibration data for incremental erosion calculation. Each of the two wells were logged with an ultrasonic measurement device while one of the two was also logged with an optical imaging tool separately. Perforation area increase for each perforation and the total area increase for each perforation cluster were analyzed to assess the impact of perforating variables on erosion of individual perforation and effectiveness of different perforating designs with respect to equal distribution. The erosion measurement using both logging techniques provided unique opportunity to compare their measurement capability (optical imaging measured 90% of fracture stages as compared to ultrasonic, but both techniques missed ~10% of total measured perforations to quantify perforation size) and range of uncertainties of measurement erosion (general variability in eroded perf diameter measurement of individual perforation by each technique, but statistically R2 of 0.95 correlation excluding 3 outliers of total 873 perforations measured). Of the variables tested a few stood out as providing more even perforation distribution than others. Not surprisingly, reducing total shot count and subsequent flow area, creating a larger pressure drop at the perforations, resulted in a more even distribution of erosion. Smaller initial hole size with an increased shot count, to provide a similar expected pressure differential, also resulted in improved cluster distribution. Variable shot count perforating achieved a more even distribution and counteracted a nature tendency for heel bias. In general perforations on the lower side of the casing showed increase erosion compared to perforations in the upper part of the casing, providing credence to the notion that sand densities are higher in the bottom of the casing even with very high sand transport velocities. This paper shares a case history with results comparison of two major erosion measurement techniques, erosion variability of each perforation as per its azimuth, and highlights some of the variables that have a large impact on cluster distribution and is of benefit to anyone optimizing a perforating design or developing a similar trial to test cluster distribution in other basins.
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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 | 0.000 |
| 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