A New Diagnostic Tool for Performance Evaluation of Heavy Oil Waterfloods: Case Study of Western Canadian Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Waterflooding is traditionally considered an unfavorable recovery method in heavy oil reservoirs. Despite this common belief, there have been very successful reported cases of heavy oil waterfloods in western Canada, with recovery factors over 40%. On this basis, a comprehensive statistical study was conducted to determine the effects of various reservoir and operational parameters on the performance of waterfloods in these reservoirs. In this study, a database of 120 operational and reservoir parameters for 177 waterfloods in Alberta and Saskatchewan was developed and analyzed. Statistical analysis of collected database and 15 different performance indices based on the studied injection-production history was conducted using partial least squares technique. This study revealed and ranked the significance of operational parameters on performance of heavy oil waterfloods. This analysis also provided a ranking of various operational and reservoir parameters on performance of waterfloods which were successfully used for dimension reduction of input parameters. In the next step, an artificial neural network technique was applied to develop performance predictive models based on the 38 parameters selected after dimension reduction. Error analysis of the developed neural network models showed an average relative error of 10% deviation from measured performance indices using the collected production and injection histories of the studied waterfloods. This paper provides details of the successful application of the partial least squares approach and the artificial neural network for developing a diagnostic tool for evaluating and predicting the performance of waterfloods in heavy oil reservoirs based on more than 50 years of heavy oil waterflooding in western Canada. The tool developed in this study is able to predict the performance of waterfloods using the 38 easily obtainable operational and reservoir parameters.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".