Effective communication in an emerging CCS industry – an application of the SPE SRMS to convey contingencies, uncertainties, commerciality, and progress to project approval
Bibliographic record
Abstract
At COP28, Carbon Capture, Utilization, and Storage was declared a removal technology whose application must be accelerated. In 2017, the Society of Petroleum Engineers (SPE) published the CO2 Storage Resource Management System (SRMS) as a framework for stakeholder communication. The SRMS guidelines were designed to support the creation of reports that inform readers regarding the contingencies that must be overcome to obtain project approval, uncertainty in the mass of CO2 that can be stored, and the commerciality of the project. However, working examples where the SRMS has been used to communicate project status in the public domain are limited. In 2023, the authors of this paper referred to the SRMS as the standard for completing an expert report, termed a Competent Persons Report (CPR), for the Viking Carbon Capture and Storage (CCS) development in the United Kingdom. During the application of the SRMS framework, challenges were identified in conveying the progress made by the project proponent in identifying and addressing technical risks. This paper describes innovations developed to supplement the SRMS guidelines and improve communication with stakeholders. First, a flowchart was developed to explain a vital interpretation of the SRMS. Next, a defined framework of technical milestones was designed to assess the maturity of a given technical topic within the SRMS. This framework was combined with radial data visualization. The combination allowed multiple storage sites to be compared simultaneously and allowed stakeholders to quickly identify the maturity of technical analysis for any given site. We conclude that improving the consistency and clarity of CCS reporting to stakeholders can help build trust in this emerging industry.
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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 | 0.052 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".