Predicting the performance of steam assisted gravity drainage (SAGD) method utilizing artificial neural network (ANN)
Bibliographic record
Abstract
As the price of oil decreases, it is becoming increasingly important for oil companies to operate in the most cost-effective manner. This problem is especially apparent in Western Canada, where most oil production is dependent on costly enhanced oil recovery (EOR) techniques such as steam-assisted gravity drainage (SAGD). Therefore, the goal of this study is to create an artificial neural network (ANN) that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage (SAGD). The developed ANN model featured over 250 unique entries for oil viscosity, steam injection rate, horizontal permeability, permeability ratio, porosity, reservoir thickness, and steam injection pressure collected from literature. The collected data set was entered through a feed-forward back-propagation neural network to train, validate, and test the model to predict the recovery factor of SAGD method as accurate as possible. Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10% error. When the neural network was exposed to a new simulation data set of 64 points, the predictions were found to have an accuracy of 82% as measured by linear regression. Finally, the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".