Novel Benchmark and Analogue Method to Evaluate Heavy Oil Projects
Bibliographic record
Abstract
Abstract This paper presents a benchmarking strategy specific to heavy oil projects using thermal recovery methods. We use the new approach to benchmark heavy oil development projects and identify gaps in performance and suggest measures to close those gaps. Typical benchmarking studies for development projects compare Reservoir Complexity Index (RCI) against Estimated Ultimate Recovery (EUR) to evaluate the performance of the projects. Shell developed a proprietary top-quartile expected ultimate recovery (TQ EUR) tool to compare fields in primary recovery and water flood. For this study, a new method was developed to calculate the RCI for heavy oil projects. Complexity Attributes and ranking criteria were developed based on key parameters influencing thermal recovery performance. This method was used to evaluate 20 heavy oil projects worldwide. Once RCI is calculated for each project, the reservoir performance and environmental footprint were plotted against RCI to identify the top quartile fields. Oil steam ratio (OSR), EUR, and CO2 intensity were the reservoir and environmental performance metrics considered in this study. The data collected for the benchmark study included reservoir and fluid properties, reservoir geology, well operation and field development, and field performance metrics. The data sources included Energy Resources Conservation Board (ERCB), Canadian Oil and Gas Companies (Canoils), The Society of Petroleum Engineers (SPE) publications, Shell's Top Quartile Estimated Ultimate Recovery (TQ EUR) Tool and Shell's tool of recovery efficiency prediction in prospect appraisal (SWEEP). A heavy oil development project was benchmarked using the new approach to identify the gaps to top quartile performance and provide guidance and measures to close those gaps. A novel RCI framework has been developed specifically for Thermal Recovery Projects in Heavy Oil. Project benchmarking data and comparison methodology shown here can be extended to any other producing area in the world.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".