Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) using parallel pairs of horizontal wells, one drilled for steam injection and the other for oil recovery, is the most widely used and effective in-situ method for recovering the Canadian oil sands. An optimization task is used to identify the parameters that will produce either a maximum or minimum value for objective functions the user specifies. In the area of reservoir simulation, the parameters can be well spacing to identify optimal field development plan, or a steam injection pressure/rate and a liquid production rate in the SAGD process for optimal operating conditions. The objective functions may be physical quantities, such as cumulative oil produced, the recovery factor, and the cumulative steam-oil ratio, or an economic index like net present value (NPV) dependent on those physical quantities. They can also be a function independent on the physical quantities, e.g., a history match data error if the optimization task is history match. The objective of this thesis is to develop an optimizer using a genetic algorithm that can be used to optimize a variety of tasks in reservoir simulation, including the history match error minimization, the optimal field development plan, production optimization and process optimization. In this work, the genetic algorithm using both binary and continuous encoding is designed and developed, which can be coupled with a reservoir simulator to study optimization tasks in reservoir simulations. This genetic algorithm is benchmarked with the traditional gradient based optimization algorithm. The genetic algorithm optimizer coupled with a reservoir simulator is used to optimize the steam injection rates over the life of a steam-assisted gravity drainage process in a reservoir with gas cap. The parameter sensitivities of the genetic algorithm are studied.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".