Bibliographic record
Abstract
Technology Focus In recent years, an unprecedented upsurge in offshore field-development activity has been driven largely by the corresponding sustained surge in oil price on world markets. Much of this activity has been focused on deep water, where the challenges have stretched both the hardware supply chains and the availability of qualified workers close to their limits. One of the consequences of this offshore market stimulation has been a substantial increase in unit development costs to a point at which operators are increasingly seeking ways to restore a better balance in supply and demand. Hence, an increasing number of field-development decision deferments are becoming a key part of that process. One potential benefit to the industry that could be derived from a temporary slowdown in major capital expenditure would be an opportunity for increased focus on technology development and qualification in anticipation of the huge challenges ahead. The initiative, however, would have to be driven and funded largely by the operator sector, with practical vendor solutions to real, tangible problems being the prime target. Here, we take a closer look at the kind of technology developments that are already making a major contribution to our ability to move forward successfully and safely into new and harsh frontiers for oil and gas development. The chosen papers reveal the increasing importance of taking an integrated and systematic surface/subsurface approach when seeking solutions to the ever-more-complex challenges ahead. The industry has demonstrated time and again that it is not sufficient for a vendor to have put huge effort and investment into getting a new solution into its catalog of options. It is increasingly necessary to be able to show convincing and verifiable evidence that a rigorous and recognizable testing and qualification program has been followed in order to substantiate the claim that a solution is ready for field application. The final ingredient necessary to justify the technology investment is an operator capable of analyzing the qualification evidence and confident enough in its own staff to make the decision to be the first adopter of that technology offshore. Statoil has shown itself to be a shining example of such an operator in the subsea arena, but others, too, have risen to the challenge, especially where the technology is a key development enabler. I hope you enjoy reading this small selection from a large body of recent papers covering associated topics of technology-development interest. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 166639 An Assessment of the Impact of Water-Injection-Systems Uptime on Well and Reservoir Management on Two North Sea FPSOs by Olawale Adeola, Shell SPE 166617 Improving the Economics of Marginal Fields Through Technology Transfer From the Defense and Renewables Industry by Paul Watson, OPT, et al. SPE 166546 The Use of Multirotor Remotely Operated Aerial Vehicles as a Method of Close Visual Inspection of Live and Difficult-To-Access Assets on Offshore Platforms by Philip Buchan, Cyberhawk Innovations, et al.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.296 | 0.160 |
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".