Connectionist Model to Estimate Performance of Steam-Assisted Gravity Drainage in Fractured and Unfractured Petroleum Reservoirs: Enhanced Oil Recovery Implications
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) is an enhanced oil recovery technology for heavy (or viscous) oil and bitumen that involves drilling two horizontal wells in underground formations. Laboratory work, pilot-plant studies, and mathematical model development, which are generally costly, difficult, and time-consuming tasks, are taken into account as important stages in finding an effective and economical method and also predicting the performance of the SAGD technique for a certain heavy-oil reservoir. Currently, smart techniques as accurate and fairly fast tools are highly recommended for these purposes. In this work, an experimental study and an artificial neural network (ANN) linked to an optimization technique, called particle swarm optimization (PSO), were employed to obtain performance parameters such as the cumulative steam-to-oil ratio (CSOR) and recovery factor (RF) for the SAGD process. The outputs of the developed connectionist modeling (i.e., ANN–PSO) were compared with actual data, showing an average error lower than 7%, mostly because of the supremacy of the ANN–PSO method compared to the conventional ANN method and the correlations developed in this study. Furthermore, it is concluded that, among the contributing parameters, reservoir thickness and oil saturation have the most significant impacts on RF and CSOR during SAGD operations. The current study confirms the potential of hybrid connectionist modeling to screen heavy-oil fractured reservoirs for the SAGD process.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".