Practical Approach to Caprock Analysis
Bibliographic record
Abstract
Abstract The issues of caprock integrity have been thrust into the forefront of consideration and regulation. There have been several documented steam and bitumen leaks to surface and recently the AER has placed a moratorium on the licensing of shallow thermal in-situ oilsand projects. They have also sent out a series of draft requirements. In the interim, the draft letters are a good indicator of future requirements. A brief description will be made of the main technical issues in determining caprock integrity. A detailed discussion will also be made of the 5 separate documents issued by the AER. A practical approach to deal with these technical and regulatory requirements will be outlined. The foundation of all of this analysis is a good understanding of the conditions in the ground. The paper will concentrate on core acquisition, storage, analysis and preservation from the drill site to the laboratory. This is the starting point for laboratory testing, MOP determination, geomechanical modelling and thermal reservoir simulation. Failure to properly quantify the geomechanical properties and in-situ stress can increase the potential for failure. This could end a project, tie up capital, produce environmental liabilities and risk public safety. Responsible development of thermal projects requires that caprock integrity be quantified.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".