Good Tests Cost Money, Bad Tests Cost More - A Critical Review of DFIT and Analysis Gone Wrong
Bibliographic record
Abstract
Abstract Analysis of mini-frac or, as commonly referred to in North America, Diagnostic Fracture Injection Tests (DFITs), have traditionally been the sub-discipline of completion & hydraulic fracture stimulation engineers. Conducting such tests has direct and indirect costs resulting from the test itself and the extended time required for the pressure falloff, that delays the completion of the well. The benefits must therefore outweigh the costs if the test is to be justified. The value is evident as these tests are performed regularly around the world as it is one of only a few processes that can help quantify within the same test both geomechanical properties and reservoir performance drivers. The authors will present examples and lessons learned from regions around the world. In addition, the availability of a large quantity of public, high-quality data from oil & gas operators in Western Canada operating in shale and ultra-tight formations enable an assessment of the successes and failures of wellbore completions, reservoir types, and operator procedures. This treasure-trove of data will help completion engineers regardless of their basin of operations to overcome one of industries challenging questions "did the test achieve its objectives."
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".