DFIT Analysis in Low Leakoff Formations: A Duvernay Case Study
Bibliographic record
Abstract
Abstract Diagnostic Fracture Injection Test (DFIT) responses in some shale reservoirs, such as the Duvernay shale in western Canada, are not consistent with those interpreted through traditional analysis methods. Indeed, interpretation with traditional techniques may result in significantly incorrect estimates of closure pressure, pore pressure and formation permeability. The goal of this paper is to explain the observed DFIT behaviours for selected Duvernay shale wells in terms of low leakoff of fracturing fluid to the formation, activation of pre-existing fractures, and tip extension during the test. DFIT data in the Duvernay shale are analyzed using pressure transient analysis methods. Two scenarios are presented to explain the overall falloff behavior; moving-hinge closure with tip extension, and activation of secondary natural fractures. The validity of each scenario is examined using rigorous coupled flow-geomechanical simulation, geological information and geomechanical settings in the Duvernay Formation. Due to extremely low leakoff, the main mechanism affecting pressure falloff during the DFIT is pressure dissipation through the primary fracture created during injection. This results in significant tip extension or activation of secondary fractures. The fluctuations and spikes observed on G-function or pressure derivative plots are explained in the context of these scenarios. The leakoff rate varies with the pressure change, and the enhanced fracture surface area, during tip extension. Therefore, the assumption of Carter leakoff, and the traditional closure picks based on a straight-line tangent to the semi-log derivative on a G-function plot or 3/2 slope on Bourdet-derivative plot are not valid. Due to very low matrix permeability and the additional fracture length created through tip extension, it is unlikely that formation radial flow is established during the test, compromising the ability to obtain a valid pore pressure or formation permeability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".