Design, Deployment, Performance and Assessment of Downhole and Near Surface Monitoring Technology for Geological CO2 Storage
Bibliographic record
Abstract
Early carbon storage research and development efforts in Canada and elsewhere began with “value-added” projects such as CO2-enhanced oil recovery or CO2 enhanced coalbed methane, where the increase in production helps to offset the costs of CO2 and of its potential long-term storage. These projects provide a valuable opportunity to assess appropriate measurement, monitoring, and verification protocols for the geological storage component of carbon capture and geological storage technologies. Measurement, monitoring, and verification operations provide confidence that CO2 has been injected and stored in an environmentally sound and safe manner. Multiple, integrated monitoring instrumentation systems are being deployed in CO2 field demonstration research projects around the world and will provide experience that can be used in regulatory regimes for future commercial CO2 sequestration scale projects. The Pembina field was chosen from several fields within Alberta, Canada, for a geological CO2 storage monitoring pilot study, in which the injection of CO2 was combined with EOR. As part of the project, an existing wellbore within the study area was used as a dedicated observation well. The design and initial results during cementing of this observation well were reviewed. The experience of implementing monitoring technologies was analyzed in order to assess existing knowledge for deploying downhole instrumentation used for monitoring and verification of CO2 movements in the subsurface. Analysis indicates that the observation well allows direct monitoring and measurements at reservoir level of multiple variables through geophysical, geochemical, and geomechanical instrumentation, as well as the opportunity to carry out wellbore integrity studies under "in-situ" conditions. A postcement job and completion analysis that couples downhole measurements, analytical and numerical simulation was conducted to improve future installations. Downhole pressure gauges captured the dynamics of cement displacement and were key elements during post-cement job review and assessments of future well integrity. This research also include the performance assessment of the surface tiltmeter array, an indirect-nearsurface measurement technology, deployed in CSEMP—a CO2 enhanced coal-bed methane pilot project located also in the Pembina Field. The experience and analyses gained from the installations provide valuable insight for CO2 geological storage monitoring and risk/performance assessment.
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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 | 0.000 | 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 | 0.001 |
| 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".