Mini-Frac Analysis in Oilsands and their Associated Cap Rocks Using PTA Based Techniques
Bibliographic record
Abstract
Abstract The Alberta Energy Regulator (AER) requires mini-frac tests to be performed on thermal or polymer injection projects as part of the licensing process. Mini-fracture closure stress in the cap rocks is the key item used in determining the maximum operating pressure (MOP) for injection wells. The MOP has a significant impact on the economics of the project. This paper will establish a physics based interpretation method that covers all injection/fall-off mini-frac tests. In the field, mini-frac tests are typically performed with multiple injection/fall-off cycles (usually 5 to 7 cycles) on each zone. Multi cycle testing is unique to the oilsands and their associated cap rocks. Multiple zones are also tested in a well, in both pay intervals (typically McMurray sands) and cap rocks (Clearwater shales etc.). Each cycle is analyzed for closure events and hopefully a consistent stress is found for each zone. Industry currently faces a conundrum as there is no agreement among analysts as to how to interpret these tests. As a result, reports submitted to the AER follow different methodologies. This may partially explain the wide range of reported closure stresses within individual basins. This paper will show using field mini-frac data that traditional interpretation techniques can lead to ambiguities/incorrect interpretations. These issues can be overcome by using a pressure transient analysis (PTA) based interpretation approach. PTA has a long history of analyzing injection/fall-off and production/build-up tests where the rock fabric does not change during the test. The PTA interpretation methodology has remained remarkably consistent over the past 25 years. It can be summarized as follows: first identify flow regimes with a special kind of derivative plot, and second use flow regime specific specialized plots to calculate formation properties. This paper will show that PTA can now also handle dynamic fracturing (i.e. mini-fracturing) and all of its associated special cases; which include pressure dependent leak-off, height recession/transverse storage and tip extension. Mini-frac analysis is now just a sub-set of the larger PTA methodology. It has the added advantage in that a rules based procedure can be established for the entire interpretation workflow.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".