Bibliographic record
Abstract
Abstract Commercially available fiber-optic sensors provide key information for well, reservoir and infrastructure surveillance. These fiber optic systems are now considered robust and see routine application in onshore and platform wells. Engineering, economic and project management challenges, however, delay the extension of these monitoring systems into the subsea environment. We illustrate the value of fiber optic monitoring, describe where fiber optic technology has significant potential in subsea development projects and identify the significant technology gaps which challenge subsea implementation of existing in-well fiber-optic sensing systems. We then define an approach to systematically address these gaps within the time frames of many current subsea projects, while preserving the necessary integrity of the subsea system and associated project delivery. The approach borrows from the Agile project management method and focuses on staged delivery of the fiber optic technology, maturing the technology by deploying select portions and then advancing in subsequent stages to the deployment of more-complex or more slowly evolving technologies, rather than attempting to fully mature the technology off-line prior to any deployment. It operates according to the following principles:–Deploy the more easily or readily adaptable portions of the technology first.–Ensure that each incremental deployment economically provides significant benefits, meeting business and technical surveillance targets.–Ensure that each deployment delivers technology maturation learnings that can be used to evaluate newly developed technology, with direct actionable feedback into further development plans.–Ensure that project plans contain sufficient adaptability and capacity for incorporating late-breaking or unforeseen technology advances, as well as contain contingencies to back off from portions of the technology deployment should best judgment dictate. In addition to accelerating technology advance, this approach assures further progress and technology acceptance along the way. We take inspiration from an historic application of such a method: NASA's journey to safely land men on the moon. In the staged development-deployment of the Gemini and Apollo programs, NASA consistently delivered tangible results with each launch, thus capturing and maintaining the support of the public, their primary stakeholders, throughout the program, as well as quickly making significant technical strides which ensured the accomplishment of their goal. Introduction Subsea wells represent expensive, high profile and high risk investments. It's no surprise that any suggestion to include new technology, such as fiber optic monitoring, in a subsea well is quite frequently rejected almost immediately despite value recognized by subsurface engineering and geoscience experts. Like all proponents of new technology, we share in the frustration that many people experience when greeted with this response. We acknwledge and appreciatethat the rationale for such strong resistance stems from the primary commitment of project managers, technical authorities, and subject matter experts to deliver the subsea project without delay, cost overrun or addition of unmanaged or unmanageable risk. We also observe that while the in-well technology advocates might seem to be at odds with installation project management, both groups actually have the same goal: to see the installation succeed in every respect while deliverying a well with high value.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".