Resilient Field Developments That Can Accommodate Uncertainty Are the Best Solution for a Sustained Low Oil Price Environment
Bibliographic record
Abstract
Abstract The upstream industry has been unable to deliver projects successfully over the last ten years, with up to 70% of projects failing to meet schedule or cost targets. This failure rate did not matter when the oil price was high as the projects remained profitable. However, after the oil price dropped in 2015, this level of project failure has become untenable. The linear gated project management systems adopted by the industry over the last fifteen years are suitable for straightforward projects that can be well defined. However, they are not suitable for many of today's projects that are more complex and have significant uncertainty, which require a different approach. This paper describes a project management process developed in the UKCS in the 1990's that was used to bring three projects stuck for 15 years to project sanction. In addition, a recent project is described where the development was designed to accommodate a range of outcomes and by doing so allowed the project to be sanctioned with significant uncertainty still remaining. In the current environment of a sustained low oil price, across the board cuts are often implemented in an attempt to make projects economic. Arbitrary cuts on their own are unlikely to make projects viable and instead the industry needs to take a step back and question the processes that have been used and why they have failed. A different approach is suggested; one that embraces uncertainty to produce resilient projects that can accommodate change. Implementing this will require a change in mindset as much as a change in process.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".