Development as the Continuation of Appraisal By Other Means
Bibliographic record
Abstract
Abstract This paper is part of an overall programme to investigate how to distill the massive amounts of technical information that are generated in the analysis of any upstream petroleum asset into suitable formats for use in "complete" decision tree analyses of the design, management and value of that asset. A complete decision tree analyses future flexibility in response to all dynamic uncertain variables, including prices, throughout the life cycle of the asset. The first part of the paper explains how and why we have set up this programme. The second part of the paper begins an examination of a particular issue where we need to distill technical detail for use in a complete decision tree. The upstream petroleum asset life cycle is usually divided into discrete phases. However, the actual situation is more fluid. For example, information arrives, and can be collected, throughout the life of the asset. In this sense, appraisal never stops, and, in principle, development and production activities should be tuned to take into account the value of the information that can be collected as a result of these activities. We begin to explore this by examining, in an integrated fashion, the appraisal and development phases of the asset life cycle, using a model of an offshore oil-field development lease as an example. We presume that drilling and facilities construction are the only two activities during this part of the life cycle. Drilling can give information about the asset ("appraisal drilling") or provide production capability ("production drilling") or both. Investment in production facilities begins at sanction, which can occur in any year until the end of the lease. In our initial exploration of this issue, we have found situations where an asset manager can add value by considering the option to have mixed appraisal and production drilling programmes before and after sanction.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".