VALUING PUD RESERVES: A PRACTICAL APPLICATION OF REAL OPTION TECHNIQUES
Bibliographic record
Abstract
Discounted Cash Flow (DCF) tools are fundamental to engineering and financial analysis in the oil industry, are well understood by managers, and generally provide accurate valuations of developed hydrocarbon reserves. Unfortunately, DCF techniques systematically undervalue proven undeveloped reserves (PUDs), may encourage premature development of certain reserves, and fail to identify important risk management opportunities. Real option valuation models overcome these shortcomings by providing a more complete picture of not only reserve values, but also of the drivers of that value. The authors of this paper collaborated in developing a PUD real option model for a large U.S. E&P company (referred to as “XYZ Petroleum”). Based on an analysis of XYZ's drilling costs and other major inputs over a 12‐year period, the authors show that PUDs are rich sources of option value. In addition to the volatility of oil and gas prices, a somewhat more surprising contributor to option value was the lack of correlation (which came as a surprise to XYZ's managers) between development costs and oil prices. During certain periods, the economic value of a PUD was more than twice the NPV estimated by static DCF techniques. In addition to valuing PUDs and explaining why undeveloped reserves are usually valued at more than their DCF value, the model can also be used to tell managers when is the value‐maximizing time to drill—or, alternatively, how much value is likely to be forfeited if managers choose to drill too soon.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".