A Management Decision Tool for Ranking Oil Sands Resource Development Opportunities
Bibliographic record
Abstract
Abstract A management decision-making and planning tool has been developed to provide a quick, high-level resource quality assessment of oil sands assets to enable economics-based ranking of investment opportunities. It is systematic and transparent, largely avoiding the subjectivity and human bias often associated with ranking assets for capital allocation. The model utilizes the available reservoir characterization information (API gravity and petrophysical analysis including oil saturation, effective porosity, V shale, pay thickness), expected operating conditions (steam injection pressure, horizontal well lengths), and a reservoir risk assessment to predict the key performance metrics for an in situ oil sands project using SAGD (steam assisted gravity drainage) including oil rates, steam-oil ratio and recovery factors. The reservoir risk factor is a quantification of the production impact (lower expectations and/or increased uncertainty) from reservoir impairments based on expert opinions and reservoir simulation. These performance metrics can then be used to estimate expected overall economic potential (IRR) for a given asset. The ranking can be done at various levels: land sections, defined prospects, wellpad drainage areas, or at the individual (delineation) well level. Predictive analytics techniques, in this case multi-variable linear regression, were used to construct the model. It was initially based on thermal recovery theoretical models for the SAGD process for predicting oil rates and a simple energy balance for predicting steam-oil ratio (SOR). Subsequently it has been updated via industry production data "fitting", or applying the actual performance data of various mature, operating wellpads to improve the confidence level of the model. The result is a hybrid model; science-based but influenced by real operating and production experience. It has served as a primary tool used for strategic planning, in the setting of high-level performance targets (and probabilistic distributions thereof) for each of the assets. This tool has enabled a resource driven development strategy, allowing the company to focus technical resources on the assets that possess the greatest economic potential. Resulting business decisions include capital allocation (for additional delineation data) and more rigorous technical efforts (reservoir modeling and simulation) on the highest ranking prospects.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".