Improving Stochastic Evaluations Using Objective Data Analysis and Expert Interviewing Techniques
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.
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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 | 0.054 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".