The Plains CO2 Reduction (PCOR) Partnership: CO2 Sequestration Demonstration Projects Adding Value to the Oil and Gas Industry
Bibliographic record
Abstract
Abstract Carbon capture, utilization, and storage (CCUS) in geologic media have been identified as an important means for reducing anthropogenic greenhouse gas emissions into the atmosphere (Bradshaw et al., 2006). Several geologic settings are appropriate for geologic storage of carbon dioxide (CO2), including depleted oil and gas reservoirs, deep brine-saturated formations, CO2 flood enhanced oil recovery (EOR) operations, and enhanced coalbed methane recovery. The U.S. Department of Energy (DOE) is pursuing a vigorous program for demonstration of CCUS technology through its Regional Carbon Sequestration Partnership (RCSP) Program, which entered its third phase (Phase III) in October 2007. This phase is planned for a duration of ten U.S. federal fiscal years (October 2007 to September 2017), and its main focus is the characterization and monitoring of large-scale CO2 injection into geologic formations at CCUS sites. The Plains CO2 Reduction (PCOR) Partnership, led by the Energy & Environmental Research Center (EERC), is one of seven regional partnerships established under the RCSP Program. The PCOR Partnership region includes all or part of nine U.S. states and four Canadian provinces (Figure 1) and is made up of numerous private and public sector groups (Figure 2) working to identify the most suitable CO2 storage strategies and technologies, aid in regulatory development, educate the general public, and investigate appropriate infrastructure for CCUS commercialization within its region. The Phase III program undertaken by the PCOR Partnership includes two commercial-scale projects that are of immediate interest to the oil and gas industry, namely the Fort Nelson carbon capture and storage (CCS) feasibility project and the Bell Creek combined CO2 EOR and storage project.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".