Numerical Assessment of the Maximum Operating Pressure for SAGD Projects Considering the Effects of Anisotropy and Natural Fractures in the Caprock
Bibliographic record
Abstract
Abstract This paper presents a numerical assessment for the Maximum Operating Pressure (MOP) of a Steam Assisted Gravity Drainage (SAGD) project considering the effect of the Natural Fractures (NFs) and intrinsic anisotropy of the cap shale. Current numerical and heuristic assessments usually ignore the effect of the intrinsic and structural anisotropy of the cap shale in the caprock integrity studies. A coupled Hydro-Thermo-Mechanical (HTM) model was developed to assess the MOP. The coupled model employed a novel constitutive model which was developed to investigate the effect of NFs and intrinsic anisotropy in the cap shale. The coupled model was validated against surface heave measurements, and later utilized in a sensitivity study to assess the MOP for cases with different number of NF sets, fracture density and fracture dip angle. Results indicate that the MOP is highly sensitive to the fracture density and dip angle. According to the results, vertical fractures have minor effect on the MOP while oblique fractures with the dip angle between 25° to 65° significantly affect the MOP. Neglecting the NFs can lead to significant overestimation of the MOP. This highlights the necessity to include the NFs in the caprock integrity assessments. The numerical model presented in this paper considers the intrinsic anisotropy and the presence of NFs in the cap shale, while existing mathematical tools for caprock integrity studies have not incorporated the intrinsic and structural anisotropy. Ignoring the anisotropy in caprock can potentially cause a considerable overestimation of the MOP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".