Probabilistic Analysis on the Caprock Integrity During SAGD Operations
Bibliographic record
Abstract
Abstract Geomechanical simulations are often used to study the deformation and mechanical failure behaviour of caprock formations in a SAGD operation. Different assumptions are made when building the simulation models. Usually the overburden formations above the reservoir are described by a limited number of vertically separated layers with varying thickness. Each layer is assumed homogeneous with laterally uniform material properties. In many cases, these simplifications are not accurate. It is important that the simulations properly consider the vertical and lateral variations. This paper presents a probabilistic analysis on caprock integrity using a randomized finite element simulation approach. Volume of shale (VSH) logs and X-ray Diffraction (XRD) analysis data are used to generate stochastic distribution of 3 major litho-facies: sands, shale with low illite/smectite (I/S) contents and shale with high I/S contents. The litho-facie distributions are then mapped onto the finite element discretization of simulation models. Mechanical properties of the 3 major litho-facies are measured in a geomechanical laboratory test program. The geomechanical finite element model is coupled with thermal reservoir simulation results to study the deformation behaviour of caprock during a SAGD operation. Following a number of simulations, the results are statistically analyzed to estimate the probability of the caprock to enter plastic yielding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".