A Risk-Based Monitoring Plan for the Fort Nelson Feasibility Project
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Bibliographic record
Abstract
Abstract The Plains CO2 Reduction (PCOR) Partnership and Spectra Energy Transmission (SET) are investigating the feasibility of a carbon capture and storage (CCS) project near Fort Nelson, British Columbia, Canada. The project aims to reduce carbon dioxide (CO2) emissions from SET's Fort Nelson sour gas-processing plant by injecting up to 2 million tonnes of sour CO2 (approximately 95% CO2, 4% hydrogen sulfide [H2S], and 1% methane [CH4]) a year into a deep mid-Devonian-age carbonate reef for long-term geologic storage. The Fort Nelson CCS project provides a unique opportunity to develop a set of cost-effective, risk-based monitoring techniques for large-scale storage of sour CO2 in deep saline formations. An approach is being developed that integrates characterization, modeling, risk assessment, and monitoring into an iterative process to produce superior quality results during each phase of the project. During the preinjection phase of the project, the characterization activities are used as input to the modeling effort. The results of the modeling and characterization activities are used as input to the first-round risk assessment, which helps identify knowledge gaps and project risks. The output from the risk assessment is then used to guide further characterization efforts and develop the monitoring plan. Once injection begins, the monitoring program results will be compared to the modeling predictions. The models will be adjusted as necessary, and new simulations will be run to predict the movement of the injected sour CO2 in the reservoir. Predictions that closely match the monitoring data will strengthen the project by 1) demonstrating that the modeling can be used to accurately aid in risk identification, 2) providing insight into long-term stability of the CCS system, 3) helping to ascertain when closure conditions have been met in the postinjection phase, and 4) enabling the CCS operator to obtain CCS project closure certification. Although specific techniques and procedures may change as the project proceeds, this philosophy of integrated characterization, modeling, and risk assessment will ensure that monitoring strategies remain fit for purpose, cost-effective, and efficient throughout the life of the project.
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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 | 0.000 | 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 it