Unconventional Risk and Uncertainty: Show Me What Success Looks Like
Bibliographic record
Abstract
Abstract The translation of risk from conventional to unconventional resources moves the fulcrum from risk of finding, towards risk of commerciality. We are often certain a hydrocarbon producing system exists; but, uncertain in what manner (production profile) it will produce. This reality is incompatible with traditional methods of risking in which recoverable volumes are estimated from volumetric calculation, and risked volumes may be calculated as the product of unrisked volumes and some risk factor. Production profile is then determined after-the-fact. Unconventional resources are typically evaluated from the basis of a type well or production analog first, and then the recoverable volume is emergent from this production profile. Like the chicken-and-egg problem, uncertainty of recoverable volumes must be estimated in association with one or more production profiles. The threshold of commerciality can be calculated, and the chance of commercial success determined by the intersection of the threshold volume on the distribution of recoverable volume. This distribution is truncated and the outcomes above the threshold are rescaled to represent success outcomes… but, failure outcomes are disregarded. This paper presents a simple workflow using Frequentist and Bayesian approaches, respectively, for proper risking of uncertain recoverable volumes for an unconventional resource, taking into account the chance of false positives from appraisal well information. A subjective risk tolerance can be included to respect how aggressive or conservative a company may be in pursuit of a project. A method of re-casting production profiles is demonstrated as an improvement over the common method of scaling, or factoring, the initial production rate. The scaling approach may reach unrealistic values of initial production in an attempt to achieve the proper recoverable volumes. Productive yet uneconomic outcomes should be included in any thorough project evaluation. Failure to do so may overestimate the value of a given project. However, the chance of such outcomes occurring is often seen as a difficult-to-determine value. Quantifying the uncertainty in type well volumes allows evaluation of the imperfectness of information of early well results. Enhanced knowledge of the project risk, the chance of success, a view of the success case, and an understanding of false positive outcomes, can lead to higher quality decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".