Modeling of Simultaneous Proppant Fracture Treatments in the Fruitland Coal and Pictured Cliffs Formations in the San Juan Basin
Bibliographic record
Abstract
Abstract Limited entry design techniques have proven successful for the simultaneous fracture stimulation of the Fruitland Coal and the Pictured Cliffs sandstone in the T28N-R7W Federal Unit of the San Juan basin, Rio Arriba County, New Mexico1. Optimization of this completion technique is dependent upon determining and placing the required effective propped fracture length in the coal and sandstone formations. This manuscript addresses utilizing limited entry techniques and a 3D fracture model. The model is then used to design N2 foam proppant fracture treatments in the coal and sandstone formations. This completion methodology allows reserves to be recovered from the Fruitland Coal at a significant cost reduction. With the different mechanical and reservoir properties of the two formation types, created fracture geometry will vary in each formation. Methods used to model these varied fracture geometries are discussed. Net pressure and production data analysis provide estimates for the effective propped fracture lengths in each formation. Radioactive tracer and production logs are presented as supporting evidence to validate the well completion design, the fracture modeling inputs and the stimulation of both formations simultaneously. Using this fracture modeling technology leads to increased reserve recovery and cost effective proppant fracture treatments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".