Subsurface Containment Assurance Program - Key Element Overview and Best Practice Examples
Bibliographic record
Abstract
The goal of Subsurface Containment Assurance (SCA) is to ensure that no adverse environmental impact, damage to operated assets, or impacts on well operations (drilling or production) occur due to leakage of production or injection fluids from reservoir intervals. Subsurface Containment Assurance involves the integrated efforts of subsurface (reservoir and overburden characterization), wells (planning, construction, well integrity and abandonment), operations (process safety and well operations and management of change) and HSE (health, safety and environment) teams. Disciplines must act together to develop and implement a surveillance plan to proactively monitor containment during well and injection operations in offshore fields. The paper will describe the elements of a Subsurface Containment Assurance Program (SCAP) that are required for business units operating offshore across the entire life cycle from exploration to mature developments. The program is designed to be comprehensive, yet flexible; and focuses on the critical elements and risks for individual operating units. A consistent framework needs to be created and implemented that draws from existing tools for reservoir and overburden characterization and field management, and combines these tools to reduce the risk of unintended subsurface fluid containment loss. Specific assessment criteria and ranking approaches and tools for qualitative and quantitative estimation of containment risks will be discussed. Finally, surveillance programs focusing on containment using both direct and indirect measurements will be highlighted with a focus on offshore data gathering. Loss of containment puts the environment, operating equipment and personnel at risk. Proactively identifying and mitigating containment risks is critical to operate safely. Containment assurance, particularly offshore, needs to be a key element of any asset and subsurface management plans.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".