Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network
Bibliographic record
Abstract
This study presents a successful application of multivariate partial least-squares (PLS), response surface methodology (RSM), and artificial neural network (ANN) to develop a new diagnostic tool for performance prediction of waterflooding in heavy oil reservoirs. The data used in this study consist of 120 operational and reservoir parameters for 177 waterfloods in heavy and medium oil reservoirs in western Canada (i.e., Alberta and Saskatchewan). This study also used 15 numerically devised indices for performance evaluation of water injection, based on collected injection and production histories of studied waterfloods. To reduce the number and complexity of input parameters of ANN models, a comprehensive PLS analysis was conducted. In addition to parameter selection, the PLS model provided a more in-depth understanding of differences between heavy and medium oil waterfloods. Next, RSM was used to improve the quality of the database for a selected combination of 38 reservoir and operational parameters by predicting some of the missing data. Finally, ANN models were developed using the feed-forward backpropagation algorithm with momentum for error minimization. The developed models show the superior ability of the ANN for creation of an efficient reservoir engineering tool for fast performance prediction of waterfloods using easily obtainable operational and reservoir parameters. The developed models in this study can be incorporated into reservoir engineering, risk assessment, and production optimization software programs to improve the quality of predictions based on more than 50 years of waterflooding experience in western Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".