Estimating pore fluid saturation in an oil sands reservoir using ensemble tree machine learning algorithms
Bibliographic record
Abstract
This thesis aims to estimate pore fluid saturation values in an oil sands reservoir using ensemble tree based machine learning models.Oil sands reservoirs provide an interesting opportunity to explore a relatively new technique in petrophysical analysis.The specific reservoir used in this study has high heterogeneity with discrete muddy layers that are difficult and time consuming to incorporate into a conventional petrophysical model.In addition, due to strong well control and sufficient well log data, the reservoir is a perfect candidate to test out a data-driven model by using techniques in Machine Learninga subfield of Artificial Intelligence.Specifically, Random Forests and Extreme Gradient Boosted Trees are combined, which are two different ways to implement a decision-tree based model structure.The two algorithms have rapidly gained popularity in the machine learning community due to their robustness when dealing with outliers and/or bad data combined with a comparative immunity against over-fitting.The final aim of this thesis is to obtain comparable or superior results to the Modified Simandoux Equation method and analyze the shortcomings and advantages of the two methods in a real petroleum field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".