VALUING PUD RESERVES: A PRACTICAL APPLICATION OF REAL OPTION 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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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