Improving Stochastic Evaluations Using Objective Data Analysis and Expert Interviewing Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Probabilistic treatment of parameters in economic analysis has become widespread in the petroleum industry. Methods consist of either Monte Carlo simulation or approximations of full stochastic distributions for use with decision tree analysis of varying complexity. Unfortunately, however, although many agree that proper quantification of uncertainties is critical for stochastic evaluation of project economics, post audits indicate the description of key variables to be suboptimal. Studies across multiple industries confirm that it is more common for actual parameters used in project economics to fall outside of their predicted ranges, than inside. Many suggest this to be due to both motivational and cognitive biases – repeatedly resulting in commonly stated ranges, which are too narrow. The authors contend that narrow, or sub optimal, variable ranges often result from a lack of use, or misuse, of available data and methodology to counteract inherent bias. In exploration prospect and play valuation, where lack of direct data is an issue, analogous data may be used, but may not be well understood, or well thought through as to how well it represents the prospects and plays the practitioner is attempting to describe. In fact, in the absence of data, many use distributions not commonly found in nature. Hence, it is the goal of this paper to present a heuristic overview of subsurface parameter distributions for commonly used properties in stochastic economic analysis, and describe an expert interviewing methodology to improve variable descriptions. The authors propose that it is the combination of both relevant objective data and quantification of subjective uncertainty that will improve variable descriptions. This paper presents the results of a survey of some of the exploration/production areas in North America, along with the distributions encountered and properties of those distributions, from empirical data. Examples are from the Gulf of Mexico (GOM), Permian Basin, and the Western Canadian Sedimentary Basin, with a view to gain insight into the distributions and trends of the properties of key subsurface variables. In addition, this paper presents the probability method to achieve better descriptions of variables. The authors suggest that although several subjective uncertainty assessment methods exist, the probability method is preferred due to its ability to counteract biases. This paper describes the steps of the probability method for range variable assessment, and explains the tools and rationale for each step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it