Expanding Interpretation of Interwell Connectivity and Reservoir Complexity through Pressure Hit Analysis and Microseismic Integration
Bibliographic record
Abstract
Abstract This paper presents a multi-disciplinary workflow for analyzing interwell hydraulic fracturing pressure interactions on multi-well horizontal pads in unconventional reservoirs. Over twenty wells in multiple fields with varied spacing across multiple landed zones are evaluated. The workflow provides a method for determining the degree of connectivity between the wells to assess the extent and complexity of the stimulated network. The analysis method provides a cost efficient, timely means of understanding the stimulated network in order to impact decisions regarding well spacing, injection rate, perforation design and frac order. Prescriptive completions programs enable observation of pressure interactions between wells during multi-stage hydraulic fracturing. Wellhead pressures are continuously recorded during all completion and flowback operations. In the observation pads studied, wells experience varying degrees of pressure communication across the fracture network. Pressure hits are grouped by according to identifying characteristics and correlated to microseismic data where available. Characterization of the stimulation network gained from analysis of pressure interactions closely aligns with available high resolution microseismic data. Networks are shown to have significant vertical and lateral growth establishing a highly complex network. Additional insights on the degree of connectivity and the definition of effective fracture network are gained. Results are fundamental to understanding well spacing and zonal placement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".