Effect of Simultaneous and Facies-based inversions on Geomechanical properties estimations in an Unconventional Reservoir
Bibliographic record
Abstract
Summary In this work, we present the comparison of results from geomechanical properties estimations, e.g. maximum shear stress, and collapse pressure, using the elastic property volumes from three seismic inversion datasets: 1) Simultaneous Seismic Inversion using all wells available, 2) Simultaneous Seismic Inversion removing a well from the low frequency model building where a DFIT sample exists, and 3) Facies-based inversion. The comparison quantifies the impact of the variation of the absolute elastic parameters results of the three seismic inversion datasets on the estimation of geomechanical properties of the reservoir, and, therefore, their impact on drilling decisions (e.g., mud weight, shear stress avoidance). Only datasets 1 and 2 used a low-frequency model. The geological formation of interest is the Lower Montney, located between the provinces of Alberta and British Columbia in Canada. A pre-stack seismic with angles up to 45 degrees and eight wells were available. The range of collapse pressure values were 10–16 MPa and 5–12 MPa for maximum shear stresses in the reservoir. There is a relationship with low values of shear stresses and high values of collapse pressure estimates. The distribution of values is different for each of the seismic inversion datasets.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".