Case History: Observations From Diagnostic Injection Tests in Multiple Pay Sands of the Mamm Creek Field, Piceance Basin, Colorado
Bibliographic record
Abstract
Abstract The objective of diagnostic fracture injection tests is to optimize multi-sand completions by estimating pore pressure and permeability in each lenticular sand. Since December 1998, over 200 diagnostic fracture injection tests have been pumped into isolated Piceance Basin Mesaverde sands in an effort to optimize well completions. The diagnostic injection tests were implemented to qualitatively identify the presence of fractures/fissures, to provide estimates of pore pressure and permeability, and to optimize the perforation scheme for effective limited entry fracture treatment diversion. This paper describes the diagnostic fracture injection test methodology, summarizes the results, and provides illustrative examples of typical Mesaverde sandstone pressure falloff response. The results from 201 diagnostic fracture injection tests show extreme differences in reservoir quality between sands with very similar openhole log signatures. While approximately half of the injection tests indicate open fractures/fissures through pressure-dependent leakoff, it is quite common to observe relatively high-permeability (kg > 0.050-md) without indications of open fractures/fissures. Permeability estimates between pay sands separated by less than 50-ft have been observed from kg < 0.001-md to kg > 0.100-md. Several examples of sands damaged by drilling mud invasion (fracturing) have also been verified with the diagnostic injection test analysis, which can differentiate between reservoir fluid permeability and fracture face damage. Since the differences in reservoir quality are identified with a pre-frac diagnostic injection test, the final perforation scheme can also be adjusted to ensure a limited entry fracture treatment will divert to the "best" reservoir rock. Although the results presented are specific to the Piceance Basin, the methodology and analytical techniques are valid in all basins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".