Pore Pressure Modeling and Stress Faulting Regime Determination of the Montney Shale in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Abstract This work establishes an effective approach to predict pore pressure in the overpressured Montney Shale and overburden from sonic logs by implementing normal-trend and explicit methods. The cause of the overpressure condition in the Montney is also addressed. These two methods were selected on the basis of the study carried out by Contreras et al. (2011) that worked successfully for pore pressure prediction under subpressured conditions in parts of the Western Canada Sedimentary Basin (WCSB). As a second objective, the stress faulting regime was determined in the study area by using Stress Polygons and data from diagnostic fracture injection test analysis as a quantification of the minimum horizontal stress. This is of paramount importance since there is not a generic theory about the stress faulting regime for most of the west region of the WCSB. The Eaton method from sonic logs (Eaton, 1975) and the Bowers method (Bowers, 1995) were implemented in two vertical wells drilled through the Montney shale. The first part of the analysis considered two normal compaction trends but unreasonable pressure profiles were obtained and required a revision on the depositional environment. It was found that for the study area three normal compactions trends have to be considered. The Bowers method was initially implemented using both loading and unloading conditions in order to establish a safe range of pore pressure to allow successful well plans. It is concluded that undercompaction could be masked as the only overpressure mechanism in the Montney shale in the study area. The formation experiences an inverse faulting regime that will lead to the creation of horizontal hydraulic fractures. The Eaton method using three normal compaction trends and an exponent equal to 0.9 works successfully in the study area. The Bowers method using the loading and the unloading conditions, and the specific correlation parameters were found to be suitable for the study area and can be extrapolated to adjacent future production and exploratory wells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".