The Use of Pressure Transient Analysis Tools to Interpret Mini-Frac Data in Alberta Oilsands Caprocks
Bibliographic record
Abstract
Abstract The use of pressure data to understand reservoir flow characteristics has been evolving since Darcy's Law was published in 1856. Welltesting has evolved from straightline methods to sophisticated interpretation models which use Log-Log pressure and derivative diagnostic plots to recognize flow regimes. This allows analysis to be performed on a valid subset of the data. It has long been known that reservoir fluid flow alone will very rarely if ever create a Bourdet derivative slope of greater than one. Mohamed et al (2011) showed that for tight shale gas mini-fracs, the derivative slope during fracture closure is equal to 3/2. This diagnostic signature is readily apparent in Mini-Frac tests of the Alberta oilsands caprock which flags it as a nonreservoir geomechanical effect. Mattar (1997) showed reservoir flow has a characteristic shape on the primary pressure derivative plot: the primary pressure derivative data continuously declines for all reservoir flow regimes. The primary pressure derivative appears to stop declining while the fracture is closing and resumes declining after the fracture is closed (it can increase for the forced closure case). Barree et al (2009) uses G Function and Square Root plots and their derivatives to indentify flow regimes as the basis for establishing fracture closure. This sounds familiar to Bourdet's pressure derivative rationale. This paper shows these methods compliment conventional PTA and give consistent repeatable mini-frac solutions for oilsands caprocks. The incorporation of fracture solutions into PTA programs is another step forward to delivering reliable, consistent analysis for mini-fracs, however, these solutions only exist for falloff whereas the 3/2 slope and primary pressure derivative also work for flowback assisted fracture closure. Several examples will be presented to illustrate that Pressure Transient Analysis is a useful tool for resolving Mini-frac data, especially the start and end of fracture closure.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".